diff --git a/README.md b/README.md index c4a19eb..3fd839d 100644 --- a/README.md +++ b/README.md @@ -52,7 +52,7 @@ nieliniowe kola, port szeregowy). | `bag_to_rtabmap.py`, `render_map_preview.py` | mapa ogrodu RTAB-Map (issue #7, odlozone) | | `so101_urdf/` | model URDF ramienia | | `deploy/` | `setup_pi.sh` (instalacja na Pi), `push_to_pi.sh` (wyslanie kodu), udev, autostart panelu | -| `docs/` | STATUS (stan), SETUP, RUNBOOK, HARDWARE, LOG (dziennik sesji), CONTRIBUTING (zasady) | +| `docs/` | STATUS (stan), SETUP, PANEL, RUNBOOK, HARDWARE, LOG (dziennik sesji), CONTRIBUTING (zasady) | | `legacy/` | odlozone: IK i slepy podjazd, nagrania sprzed naprawy barku, stare skrypty jazdy ([legacy/README.md](legacy/README.md)) | | `requirements-pinecone.txt` | zaleznosci laptopa (numpy, opencv, pyserial, pytest); Pi: `requirements-pi.txt` | @@ -61,6 +61,7 @@ nieliniowe kola, port szeregowy). - [docs/STATUS.md](docs/STATUS.md): stan projektu, aktualizowany w kazdym PR. - [docs/LOG.md](docs/LOG.md): dziennik sesji, tylko dopisywanie. - [docs/RUNBOOK.md](docs/RUNBOOK.md): jak odpalic robota krok po kroku. +- [docs/PANEL.md](docs/PANEL.md): polaczenie z Pi i odpalenie panelu jazdy + ramienia. - [docs/SETUP.md](docs/SETUP.md): instalacja laptop / Pi. - [docs/HARDWARE.md](docs/HARDWARE.md): sprzet i pulapki. - [docs/CONTRIBUTING.md](docs/CONTRIBUTING.md): jak pracujemy. diff --git a/arm_control.py b/arm_control.py index d3a7bc0..714d02a 100644 --- a/arm_control.py +++ b/arm_control.py @@ -38,12 +38,12 @@ # z fix_shoulder_offset.py (2026-09-25). Po kazdej zmianie kalibracji spisz na nowo # przez `./arm.sh status` w zlozonej pozie. HOME_POSE = { - "shoulder_pan": 1.27, - "shoulder_lift": -85.05, - "elbow_flex": 99.0, - "wrist_flex": -102.11, - "wrist_roll": 89.10, - "gripper": 1.69, + "shoulder_pan": -5.45, + "shoulder_lift": 88.92, + "elbow_flex": 7.56, + "wrist_flex": -87.87, + "wrist_roll": 88.88, + "gripper": 41.06, } # Limit ruchu na jedno wywolanie send_action (stopnie / jednostki motoru) - zabezpieczenie diff --git a/docs/ACT_TRAINING.md b/docs/ACT_TRAINING.md new file mode 100644 index 0000000..08e7016 --- /dev/null +++ b/docs/ACT_TRAINING.md @@ -0,0 +1,98 @@ +# ACT krok po kroku: nagranie -> trening -> chwyt na robocie + +Dwa etapy chwytu: ACT (siec) robi TYLKO chwyt szyszki (HOME -> szyszka -> zamkniecie -> uniesienie), +wrzut do sloika robi nagrany ruch `motions/drop_box.json` (na Pi). Laczy je `tools/act_pick.py`. + +Adresy: Pi = `robot@172.20.10.4` (hotspot "iPhone pawel"; gdy nie odpowiada: `hostname -I` na Pi). +Kamera na ramieniu: D435, serial `030522070668`. Datasety na Pi: `~/datasets/`. + +## 1. Pi: nagrywanie epizodow (leader) + +Leader wpiety w Pi jako `/dev/robot-leader` (zamiast kabla hovera - Pi nie ma wolnego USB). +Sprawdz: `ls -l /dev/robot-*` -> `robot-arm` i `robot-leader`. Zasilanie Pi mocne (`vcgencmd get_throttled` = `0x0`), +inaczej kamera gubi klatki (`frame is too old`), a spadek napiecia potrafi uszkodzic pliki venv. + +``` +pkill -f lerobot; pkill -f arm_web.py; pkill -f web_control.py +cd ~/hackaton && SVT_LOG=1 .venv/bin/lerobot-record --robot.type=so101_follower --robot.port=/dev/robot-arm --robot.id=so101 --robot.cameras="{ wrist: {type: intelrealsense, serial_number_or_name: 030522070668, width: 640, height: 480, fps: 30}}" --teleop.type=so101_leader --teleop.port=/dev/robot-leader --teleop.id=so101_leader --dataset.repo_id=local/so101_grasp --dataset.root=/home/robot/datasets/so101_grasp2 --dataset.push_to_hub=false --dataset.num_episodes=50 --dataset.episode_time_s=15 --dataset.reset_time_s=5 --play_sounds=false --dataset.single_task="Grasp the pine cone and lift it" +``` + +- Podglad kamery w czasie nagrania (ten sam proces, kamera nie jest zajmowana drugi raz): zamiast + `.venv/bin/lerobot-record ...` daj `.venv/bin/python tools/cam_preview.py lerobot-record ...` (reszta bez zmian), + w przegladarce `http://:8081/`. Osobny `rs_mjpeg_server.py` NIE moze chodzic obok lerobot. +- Dokladanie do istniejacego datasetu: ta sama komenda + `--resume=true`, `--dataset.num_episodes=` ile DOLOZYC. +- Ruchy tylko po linii `Recording episode N`. `Reset the environment` = przerwa: szyszka w nowe miejsce, leader do HOME. +- Klawisze: strzalka w prawo = koniec epizodu, w lewo = nagraj od nowa, Esc = koniec (zapisuje). +- 5 miejsc szyszki x 10 epizodow. Koniec epizodu = pozycja startowa `drop_box` (ramie z przodu, nisko, chwytak zamkniety). +- Warningi `libtorchcodec` i `Svt[...]` sa niegrozne (dekoder -> pyav, log kodera wideo). + +Stan 2026-09-27 14:00: `so101_grasp2` = 50 ep., 22451 klatek, 30 fps - GOTOWY do pelnego treningu (2b). +`so101_grasp` 7 ep. (do probnego treningu 2a). + +## 2. Laptop z RTX 3070 (Franek): trening + +Raz: repo i venv wedlug `docs/SETUP.md` sekcja "lerobot do ACT" (torch cu128 + lerobot 0.6.1). + +``` +cd +git pull +.venv\Scripts\python -c "import torch; print(torch.cuda.is_available())" +``` +Musi byc `True`. + +### 2a. Probny trening (7 epizodow, sprawdza sam proces, ~15 min) + +``` +scp -r robot@172.20.10.4:/home/robot/datasets/so101_grasp C:/datasets/so101_grasp +.venv\Scripts\lerobot-train --dataset.repo_id=local/so101_grasp --dataset.root=C:/datasets/so101_grasp --policy.type=act --policy.device=cuda --policy.push_to_hub=false --steps=5000 --batch_size=8 --output_dir=outputs/act_test --job_name=act_test +``` +`loss` w logu ma spadac. Wagi z 7 epizodow nie beda dobrze chwytac - to test procesu. + +### 2b. Pelny trening (po 50 epizodach) + +Najpierw na Pi nagrywanie zakonczone (Esc albo koniec 50), inaczej kopia bedzie niepelna. + +``` +scp -r robot@172.20.10.4:/home/robot/datasets/so101_grasp2 C:/datasets/so101_grasp2 +.venv\Scripts\lerobot-train --dataset.repo_id=local/so101_grasp --dataset.root=C:/datasets/so101_grasp2 --policy.type=act --policy.device=cuda --policy.push_to_hub=false --steps=50000 --batch_size=8 --save_freq=10000 --output_dir=outputs/act_grasp --job_name=act_grasp +``` +Na RTX 3070 kilka godzin; mozna testowac posrednie checkpointy (`outputs/act_grasp/checkpoints//`). + +### 2c. Wagi na Pi + +``` +ssh robot@172.20.10.4 "mkdir -p ~/models" +scp -r outputs/act_test/checkpoints/last/pretrained_model robot@172.20.10.4:/home/robot/models/act_grasp +``` +(dla pelnego treningu `outputs/act_grasp/...` zamiast `outputs/act_test/...`) + +## 3. Pi: chwyt na robocie (wylacznik w rece) + +Nic innego nie moze trzymac portu ramienia: `pkill -f lerobot; pkill -f arm_web.py`. + +``` +cd ~/hackaton +.venv/bin/python tools/act_pick.py --policy ~/models/act_grasp --dry-run # tylko komendy +.venv/bin/python tools/act_pick.py --policy ~/models/act_grasp --skip-drop # sam chwyt ACT +.venv/bin/python tools/act_pick.py --policy ~/models/act_grasp # chwyt + sloik + HOME +.venv/bin/python tools/act_pick.py --policy ~/models/act_grasp --repeat 3 +``` + +Podglad kamery w czasie chwytu: `http://:8081/` (act_pick puszcza rollout przez `tools/cam_preview.py`, +`--preview-port 0` wylacza). Na czas wrzutu kamera jest zamknieta, obraz wraca przy kolejnym chwycie. + +Sam ruch do sloika bez sieci: `.venv/bin/python tools/arm_play.py --motion drop_box --port /dev/robot-arm --home-first`. + +## Pulapki (2026-09-27) + +- Leader ma kalibracje SKOPIOWANA z followera (decyzja), czesc osi odwrocona. Dane dla ACT i tak poprawne + (akcja = cel followera). Naprawa: `drive_mode: 1` dla odwroconych stawow w + `~/.cache/huggingface/lerobot/calibration/teleoperators/so_leader/so101_leader.json`. + NIGDY `lerobot-calibrate` z `--robot.*` (plik followera ma reczna poprawke barku). +- `lerobot[dataset]` na Pi instalowac z override torcha, inaczej uv sciagnie torch z CUDA: + `printf 'torch==2.14.0+cpu\ntorchvision==0.29.0+cpu\n' > /tmp/ov.txt` i + `~/.local/bin/uv pip install --python .venv/bin/python --override /tmp/ov.txt --index-strategy unsafe-best-match --extra-index-url https://download.pytorch.org/whl/cpu 'lerobot[dataset]==0.6.1'`. +- `Overload error` na `id_=6` przy koncu nagrania = gripper followera dociskal szyszke az zadzialalo zabezpieczenie. + Dataset jest zapisany (finalize idzie przed disconnect). Kasowanie: otworzyc gripper, wylaczyc zasilanie serw na kilka s. + Przy nagrywaniu zamykac leaderem tylko do oporu - ACT nauczy sie docisku z danych. +- Bus error przy imporcie = plik uszkodzony po spadku napiecia; `--reinstall --no-deps --no-cache` tego pakietu. diff --git a/docs/LIVE_GRASP.md b/docs/LIVE_GRASP.md new file mode 100644 index 0000000..d28ae96 --- /dev/null +++ b/docs/LIVE_GRASP.md @@ -0,0 +1,97 @@ +# Chwytanie szyszki samym ramieniem - jak kontynuowac (stan 2026-09-26 wieczor) + +Cel: robot stoi (baza sie nie rusza), ramie SO-101 z kamera D435 na przedramieniu widzi szyszke +w pozycji HOME, samo ja chwyta, wrzuca do sloika i wraca do HOME. Deterministycznie: model z demonstracji +reka (regresja), bez ML/LLM w petli. + +Kod: `tools/live_grasp/` (branch `pawel/arm-home-cam`). Skrypty ida na Pi przez `ssh ... python -` +(stdin), nic nie jest kopiowane na Pi. Uruchamiasz je z laptopa (git-bash, katalog repo) +przez `bash tools/live_grasp/pi.sh [VAR=plik] [--bg nazwa]`. + +## Co jest zrobione i co wiemy + +- HOME = poza `arm_control.HOME_POSE` (pan -5.45, lift 88.3, elbow 7.56, wrist -87.87, roll 88.88). + UWAGA: nowy HOME jest w repo, ale NIE wgrany na Pi (`arm_control.py`, `pinecone_bot/arm.py`, `motions/home.json`). + Skrypty w `tools/live_grasp/` maja HOME wpisany na sztywno, wiec dzialaja bez tego. +- Wykrywanie szyszek w HOME z glebi dziala (plaszczyzna ziemi RANSAC, szyszka = wybrzuszenie 1.2-8 cm, + chwytak wyciety maska koloru niebieskiego, kabel serwa wyciety prostokatem). Po zmroku kolor nie dziala: + maska chwytaka i wykrywanie reki potrzebuja swiatla w pokoju. +- Ruch do sloika i z powrotem: nagrany reka, dziala przy odtwarzaniu (`data/jar_cycle.json`, + `pick.py` ma go jako `JAR_UP` / `JAR_BACK`). Sloik musi stac tam, gdzie przy nagraniu (podstawa ok. -22 st). +- Model "szyszka w kamerze -> stawy chwytu" (`data/grasp_model.json`, 8 probek) jest ZA SLABY: + blad ok. 6 cm, w probach szczeki trafialy obok szyszki albo ja spychaly. Powody: polowa etykiet + z sesji 1-2 niepewna (znikalo kilka szyszek naraz), rozne style chwytu (bark 61-92 st). +- Pulapki sprzetu: serwa maja niskie P (16) - bez korekty calkujacej zostaje 2-4 st bledu (funkcja `go()` + w skryptach ja robi); podstawa ma w EEPROM zakres tylko +-23 st (1786-2308); bark stoi przy + Max_Position_Limit (88.4 st); serwo chwytaka przy scisku do 0 potrafi zniknac z magistrali - trzymac cel 3-4 + ponizej odczytu; Pi na powerbanku raz sie zrestartowal (zasilanie). +- Kamera na przedramieniu: gdy szczeki sa nad szyszka, zaslaniaja ja - korekta ze zdjec przy samym chwycie + nie dziala (probowane: `grasp_servo.py`, `servo.py`, `attempt.py`). Wyjscie: dobry model z czystych danych + (ponizej) albo kamera na maszt (STATUS, krok 1). + +## Nastepny krok: czyste uczenie (ok. 15 min) -> model -> test + +Warunki: swiatlo w pokoju, laptop i Pi w tej samej sieci (hotspot), sloik w tym samym miejscu. + +1. Sprawdz, ze Pi odpowiada i co widzi kamera: + ``` + ssh robot@robot.local uptime + bash tools/live_grasp/pi.sh snap_only.py + ``` + Zdjecie: `tools/live_grasp/out/snap_only.jpg` (lewo kolor, prawo wysokosc nad ziemia 0-6 cm). + Przed uczeniem nic nie moze trzymac kamery ani ramienia (`ssh robot@robot.local 'pgrep -af python'`). + +2. Uczenie w tle na Pi (12 chwytow, max 20 min): + ``` + bash tools/live_grasp/pi.sh teach_clean.py --bg teach + ssh robot@robot.local tail -f /tmp/teach.err + ``` + Przebieg jednej rundy (program mowi w logu, co robi): + - robot w HOME czeka, az zobaczy DOKLADNIE 1 szyszke i zadnej reki (2 zgodne zdjecia); + - szeroko otwiera chwytak i wylacza torque = "twoja kolej"; + - chwytasz ja reka, ZAWSZE tym samym stylem: bark nisko jak w HOME, pracuja podstawa, lokiec, + nadgarstek, szczeki schodza z gory. Zamkniecie chwytaka (ponizej 25, stabilnie 0.5 s, ramie > 5 st od HOME) = zapis; + - wrzucasz do sloika, otwierasz chwytak, puszczasz ramie; po 5 s robot wraca do HOME; + - kladziesz nastepna szyszke w NOWYM miejscu (blisko/daleko, lewo/srodek/prawo), reka poza kadr. + +3. Pobierz dane i dopasuj model (laptop): + ``` + ssh robot@robot.local cat /tmp/teach.jsonl > tools/live_grasp/data/teach_session3_full.jsonl + python tools/live_grasp/fit.py tools/live_grasp/data/teach_session3_full.jsonl tools/live_grasp/data/grasp_model.json + ``` + Patrz na wiersz "blad zostaw-jedna [st]": podstawa < 2 st, bark/lokiec/nadgarstek < 4 st = mozna testowac. + Wiekszy blad = wiecej probek w tych samych warunkach albo sprawdz zdjecia HOME (pole `img` w jsonl). + Do repo wrzucaj wersje bez zdjec: + ``` + python -c "import json,sys;[print(json.dumps({k:v for k,v in json.loads(l).items() if k!='img'})) for l in open(sys.argv[1])]" tools/live_grasp/data/teach_session3_full.jsonl > tools/live_grasp/data/teach_session3.jsonl + ``` + +4. Test: jedna szyszka w polu, ktore bylo w uczeniu (program odmawia poza nim): + ``` + bash tools/live_grasp/pi.sh pick.py MODEL=data/grasp_model.json + ``` + Log: wybrana szyszka, poza chwytu, odczyt chwytaka (>= 6 = trzyma), sloik, HOME. Zdjecie: `out/pick.jpg`. + Seria z poprawka po kazdej porazce (szyszka przepchnieta -> cel przesuwa sie o 70% przepchniecia): + ``` + bash tools/live_grasp/pi.sh pick_loop.py MODEL=data/grasp_model.json + ``` + +## Inne narzedzia w katalogu + +- `rec.py` - nagranie ruchu reka (torque off, stawy 30 Hz + zdjecie co 0.5 s): `REC_SECONDS` w env na Pi + nie jest przekazywany przez pi.sh - domyslnie 120 s. W tle: `pi.sh rec.py --bg rec`, potem + `ssh robot@robot.local cat /tmp/rec.out > plik`. Przyklad: `data/rec_demo_120s.jsonl` (9 cykli chwyt -> sloik). +- `replay.py` - odtworzenie nagranego cyklu (tempo x0.5): `pi.sh replay.py CYCLE=data/jar_cycle.json`. + Szyszka musi lezec tam, gdzie przy nagraniu. +- `step.py` - jeden ruch + zdjecie: `pi.sh step.py ARGS=plik.json`, plik np. `{"rel": {"shoulder_pan": 3}, "sec": 1.0}`. +- `snap_only.py` - zdjecie + pozycje stawow, bez ruchu ramienia. +- Eksperymenty, ktore NIE zadzialaly (do wgladu): `attempt.py` (celowanie w pozycji "nad" z koloru+glebi), + `servo.py` (serwo 3D z Jakobianem, zle mierzona koncowka szczeki), `grasp_servo.py` (korekta w pozie chwytu - + szczeki zaslaniaja szyszke). + +## Bezpieczenstwo + +- Przy kazdym uruchomieniu z ruchem: czlowiek przy robocie, reka przy wylaczniku. +- Przed `teach_clean.py` i `rec.py` torque jest wylaczany - ramie jest luzne, trzymaj je. +- Nie uruchamiaj `lerobot calibrate`. Nie zmieniaj limitow pozycji w EEPROM serw bez decyzji zespolu + (podstawa +-23 st to obecny limit, stare wartosci 1786/2308). diff --git a/docs/LOG.md b/docs/LOG.md index 6b13c9e..8287e9a 100644 --- a/docs/LOG.md +++ b/docs/LOG.md @@ -875,6 +875,31 @@ ustala operator na `shoulder_pan`. placo ostrzega o samokolizjach URDF w pozie n **Nastepny krok:** sprawdzic skret na robocie, ewentualnie dostroic `MAX_STEER`. **Sprzet:** nie +## 2026-09-27 - frane + Claude - ACT: leader, nagrywanie, act_pick +**Zrobione:** leader na Pi (`/dev/robot-leader`, zamiast kabla hovera), kalibracja leadera skopiowana z followera +(decyzja frane) + gripper `calibrate_joint.py`. `motions/drop_box.json` nagrany na Pi, przyciety od t8.40, powrot tym +samym torem bez postoju (15 s; kopie `drop_box_full/_oneway/_old.json`). Na Pi `lerobot[dataset]` (override torch +2.14 cpu). Undervoltage przy 2 ramionach + kamerze: `frame is too old` w lerobot-record, potem Bus error - uszkodzone +`pyarrow` i `av` (sprawdzone hashami RECORD calego venv), przeinstalowane; nowe zasilanie -> `throttled=0x0`. +Nagrane: `so101_grasp_t2` 2 ep., `so101_grasp` 7 ep., `so101_grasp2` 12+ ep. (30 fps, 449 klatek/ep.). +`tools/act_pick.py` + `tests/test_act_pick.py` (5), 150 testow zielonych. +**Nie dziala / otwarte:** czesc osi leadera odwrocona (nie poprawione, `drive_mode` w pliku leadera). Brak wag ACT. +`drop_box.json` tylko na Pi. `torchcodec` na Pi nie laduje sie (torch 2.14 vs 0.11) - lerobot uzywa pyav. +**Nastepny krok:** 50 epizodow, trening na RTX 3070, `act_pick.py --skip-drop`. +**Sprzet:** dotkniety (ramiona, kamera, pakiety na Pi) + +## 2026-09-27 - pawel120 + Claude - podglad kamery w procesie lerobot +**Zrobione:** `tools/cam_preview.py`: uruchamia komende lerobot (record/rollout/teleoperate) w swoim procesie i +serwer MJPEG na 8081 obok. Hook na `lerobot.cameras.camera.Camera.__init__` zapisuje kazda kamere, podglad bierze +`read_latest()` (peek, nie czysci `new_frame_event`, petla sterowania dostaje te same klatki). Przyczyna, dla ktorej +wczesniej sie nie dalo: kamera na wylacznosc jednego procesu, `rs_mjpeg_server.py` + lerobot = `Couldn't resolve +requests`. `act_pick.py --preview-port` (domyslnie 8081, 0 = wylacz). Testy: `tests/test_cam_preview.py` (6), +`test_act_pick.py` (+2), 158 zielonych. Laptop: prawdziwy lerobot `OpenCVCamera` -> hook -> JPEG po HTTP dziala. +**Nie dziala / otwarte:** nie sprawdzone na Pi z RealSense i `lerobot-record` (obciazenie CPU przy 10 fps podgladu). +**Nastepny krok:** na Pi `tools/cam_preview.py lerobot-record ...`, otworzyc `http://:8081/`, sprawdzic, czy +nie ma `frame is too old` (jesli jest: `--preview-fps 5`). +**Sprzet:** nie + ## 2026-09-27 - frane + Claude - trening ACT na laptopie, dataset i wagi na masterze **Zrobione:** Od teraz praca prosto na masterze (decyzja frane). Druga sesja nagrala na Pi `so101_grasp2` (50 epizodow leaderem, kamera wrist; leader skalibrowany 12:30, kamera to D435 serial 030522070668). Kopia na @@ -907,3 +932,38 @@ Stan maszyny stanow (SEARCH/APPROACH/...) w panelu jest schematem, nie na zywo: **Nastepny krok:** na Pi `deploy/push_to_pi.sh` (po merge), `python tools/robot_panel.py --autostart`, otworzyc `http://:8090`, sprawdzic kamere, jog i WASD z wylacznikiem w rece; potem ewentualnie jako usluga systemd. **Sprzet:** nie + +## 2026-09-26 - pawel120 (Claude) - robot wjechal w ramie; cofniety heartbeat z klawiszami, predkosc /2 +**Zrobione:** Po wdrozeniu PR #33 (heartbeat niosl stan klawiszy) robot przy duzym opoznieniu hotspotu wjechal w ramie i je uszkodzil. Prawdopodobna przyczyna (Claude): opoznione heartbeaty dochodza seriami, stare "W wcisniete" po failsafe znow uruchamialy jazde, a puszczenie W przychodzilo pozniej. Dead-man mierzy czas DOTARCIA wiadomosci, wiec spoznione wiadomosci wygladaja na swieze. Cofniete: heartbeat to znow goly ping (po failsafe trzeba wcisnac klawisz od nowa). `web_control.py` MAX_PWM 500 -> 250, panel ramienia krok 2 -> 1 st/tick (chwytak 4 -> 2). Zdalny STOP po awarii nie doszedl: Pi przestal odpowiadac (ping 100% strat). +**Nie dziala / otwarte:** ramie uszkodzone - ocena. `shoulder_lift` czyta 116-128 st przy zakresie +-91.6, a wedlug uzytkownika staw jest fizycznie w zakresie -> podejrzenie rozjazdu Homing_Offset w serwie vs so101.json. Dodane `tools/calibrate_joint.py` (za zgoda uzytkownika): kalibracja JEDNEGO stawu bez `lerobot calibrate` - domyslnie tylko odczyt (rejestry serwa vs plik), `--record S` (torque OFF na stawie, reczny przejazd przez caly zakres, propozycja offsetu/limitow), `--write` (EEPROM + wpis stawu w so101.json po wpisaniu TAK, z kopia pliku). Matematyka sprawdzona na przypadku z 2026-09-25 (offset 1977, zakres ~1056..3039). NIE uruchomione - Pi nie odpowiada. Jazda po hotspocie z opoznieniem > 1 s jest niebezpieczna niezaleznie od kodu: dead-man nie odroznia spoznionych komend. +**Nastepny krok:** ogledziny ramienia; Pi na dobrym zasilaniu; zanim ktos pojedzie zdalnie - znaczniki czasu w komendach jazdy (odrzucac spoznione) albo jazda tylko w zasiegu wzroku z wylacznikiem. +**Sprzet:** dotkniety (robot wjechal w ramie - uszkodzenie) + +## 2026-09-26 - pawel120 (Claude) - HOME pod kamere, chwytanie samym ramieniem na zywo +**Zrobione:** Nowy HOME = poza ustawiona recznie (odczyt `./arm.sh status`): `arm_control.HOME_POSE`, `pinecone_bot/arm.py`, `motions/home.json`; test `grasp_mid` xfail (nagrany pod stary HOME), test STOP panelu jog w dol (HOME przy gornej granicy barku). `pinecone_bot/grasp_table.py` (tabela: piksel szyszki w HOME -> pozy nad/chwyt, najblizsza probka, ruch chwytu) + `WaypointArm.play(motion)`, 7 testow. Na zywo przez ssh stdin (nic nie kopiowane na Pi), skrypty w `tools/live_grasp/`: uczenie reka srodkowej szyszki -> chwyt udany (odczyt chwytaka 6.8, szyszka w szczekach na zdjeciu); celowanie podstawa na obrazie + glebsze zejscie (elbow -4, wrist +8) -> drugi udany chwyt. Automat z kolorem 11 prob, potem z glebia 4 proby: 0 udanych. +**Nie dziala / otwarte:** (1) serwa: P=16, bark stoi na Max_Position_Limit (surowo 3052 = 88.4 st) - male komendy nie ruszaja, potrzebna korekta calkujaca (jest w skryptach). (2) serwo chwytaka przy scisku do 0 raz zniklo z magistrali ("Missing motor IDs: 6") - trzymac z mniejszym celem. (3) kamera na przedramieniu: szczeki w obrazie zaleza od nadgarstka; w pozycji "nad" przy lewej szczece jest cien stereo (glebia slepa), tam widzi tylko kolor. (4) po zmroku kolor bezuzyteczny (ekspozycja 100 ms, gain max - ciemno); glebia z laserem 360 dziala. (5) duza lezaca szyszka nie miesci sie w szczekach - spychana. (6) Moje bledy: reguly "uczenia" wyciagaly wnioski z zlych pomiarow (bark nie wykonywal komend, koncowka szczeki z mapy wysokosci liczona zle) - parametry dryfowaly zamiast sie poprawiac. Pi: raz OOM (svd bez full_matrices=False na 20k punktow). +**Nastepny krok:** przy dziennym swietle: uczyc reka 2-3 szyszki (nad/chwyt) z zapisem zdjecia z pozycji "nad"; punkt celowania brac z tych zdjec, dopiero potem `tools/live_grasp/run.sh`. Wgrac nowy HOME na Pi (scp arm_control.py, pinecone_bot/arm.py, motions/home.json). +**Sprzet:** dotkniety (ramie: ruchy na zywo, torque wylaczony na koniec; kamera: ustawienia lasera tylko w sesji) +## 2026-09-26 (noc) - pawel120 (Claude) - chwytanie z demonstracji, dom +**Zrobione:** Nagranie ruchu reka (2 min, 9 cykli chwyt -> sloik, stawy 30 Hz + zdjecia) i odtworzenie cyklu na ramieniu. Dwie sesje uczenia (robot robi zdjecie glebi w HOME, czlowiek chwyta reka, zapis pozy przy zacisku): 20 chwytow, 8 jednoznacznych. Model liniowy szyszka (kamera HOME) -> stawy, `pick.py` / `pick_loop.py` z poprawka po przepchnieciu. Proba korekty w pozie chwytu (`grasp_servo.py`) i podejscia od gory. Wszystko w `tools/live_grasp/` + `pi.sh` (ssh stdin, bez plikow na Pi), dane bez zdjec w `tools/live_grasp/data/`, instrukcja `docs/LIVE_GRASP.md`. +**Nie dziala / otwarte:** 0 udanych autonomicznych chwytow (ok. 15 prob): model ma blad ok. 6 cm (zostaw-jedna), szczeki trafiaja obok albo spychaja szyszke. Kamera na przedramieniu nie widzi szyszki, gdy szczeki sa nad nia. Etykiety sesji 1-2 niepewne (znikalo kilka szyszek naraz), rozne style chwytu. Kinematyka URDF nie zgadza sie z ramieniem (wysokosc szczek przy ziemi rozrzucona o 5 cm). Podstawa: limit EEPROM +-23 st. Pi raz sie zrestartowal (zasilanie z powerbanku). +**Nastepny krok:** `docs/LIVE_GRASP.md`: czyste uczenie `teach_clean.py` (1 szyszka naraz, jeden styl, 12 pozycji, swiatlo), `fit.py`, test `pick.py`. Alternatywa: kamera na maszt. +**Sprzet:** dotkniety (ramie: ruchy na zywo, torque wlaczony w HOME na koniec; kamera tylko odczyt) + +## 2026-09-26 - pawel120 (Claude) - poradnik odpalania panelu (docs/PANEL.md) +**Zrobione:** Polaczenie z Pi krok po kroku i odpalenie paneli spisane w `docs/PANEL.md` (hotspot iPhone / kabel, szukanie IP, dwa terminale SSH: `web_control.py` + `tools/arm_web.py`, przegladarka, konczenie pracy, tabela bledow z dzisiejszej sesji). Link w README, notka w `docs/SETUP.md`, ze WiFi na Pi juz dziala. Na Pi: 136 testow zielonych, `./arm.sh status` OK. +**Nie dziala / otwarte:** `robot-web.service` nie zainstalowany (brak autostartu). Na Pi lezy `tests/test_calibrate_target.py` z niezmergowanego brancha `claude/robot-pinecone-test-plan-e4ca8e` (6 bledow, pomijac `--ignore`). `push_to_pi.sh` bez rsync nie usuwa starych plikow. +**Nastepny krok:** zainstalowac autostart (`deploy/setup_pi.sh` krok 7) albo zostac przy recznym starcie w tmux. +**Sprzet:** dotkniety (Pi: SSH, testy, start paneli przez uzytkownika; Claude tylko odczyt stanu) + +## 2026-09-27 - pawel120 + Claude - drive_calib (kalibracja jazdy bez miarki) +**Zrobione:** `tools/drive_calib.py` + `tests/test_drive_calib.py` (9, cale tests zielone): 2x prosto (glebia +RealSense do sciany przed/po), 2x obrot (phyphox, na zmiane lewo/prawo), pytanie operatora l/p; dopasowanie prostej +pwm = p0 + s*v -> xiao_pwm_min/max i xiao_steer_min/max, znaki steer_sign i heading.sign, `--write` do configu. +**Nie dziala / otwarte:** nie uruchomione na Pi. Dopiero po napisaniu znalezione istniejace prace na niezmergowanych +branchach: `pawel/base-calibration` (base_test --measure, landmarks.py), `frane/gyro-rate-loop` (turn_loop.py: stala +tabela w->PWM nie opisze hovera), `frane/mapa-d435` (localize.py + zygzak.py, jazda po mapie RTAB-Map). Pulapka: +bez rsync `deploy/push_to_pi.sh` idzie przez scp i nadpisuje na Pi `motions/drop_box.json` (tylko na Pi) placeholderem. +**Nastepny krok:** zdecydowac, ktora kalibracja/jazda zostaje (raczej zygzak po mapie z frane/mapa-d435); Xiao +wpiac z powrotem (teraz w jego USB jest leader), test na robocie z wylacznikiem. +**Sprzet:** nie diff --git a/docs/PANEL.md b/docs/PANEL.md new file mode 100644 index 0000000..d70c8b4 --- /dev/null +++ b/docs/PANEL.md @@ -0,0 +1,20 @@ +otwierasz 2 powershelle + +w jednym: \ + +ssh robot@172.20.10.4 + + +cd ~/hackaton && source .venv/bin/activate +ROBOT_DRIVE_PORT=/dev/robot-drive ROBOT_ARM_PORT=/dev/robot-arm python web_control.py + + + +w drugim odpalasz : + + +ssh robot@172.20.10.4 + + +cd ~/hackaton && source .venv/bin/activate +python tools/arm_web.py --port /dev/robot-arm --no-home diff --git a/docs/RUNBOOK.md b/docs/RUNBOOK.md index c8a3207..dbb7f7d 100644 --- a/docs/RUNBOOK.md +++ b/docs/RUNBOOK.md @@ -63,6 +63,7 @@ Kolejnosc ma znaczenie: kazdy krok zapisuje cos, z czego korzysta nastepny. | `tools/calibrate_hsv.py` | Interaktywne strojenie progu HSV, zapis do configu. | | `tools/calibrate_target.py` | Pomiar `cfg.cx` i `grasp.target_row` dla kazdego nagranego chwytu. | | `tools/record_waypoints.py` | Nagrywanie ruchu ramienia (waypointy) recznym ustawianiem serw. | +| `tools/calibrate_joint.py` | Kalibracja jednego stawu (np. `shoulder_lift`) zamiast `lerobot calibrate`: odczyt, `--record`, `--write` z kopia. | | `tools/frame_check.py` | Zera i znaki stawow lerobot vs URDF przez FK placo, werdykt operatora na staw (docs/ARM_FRAMES.md). | | `tools/hand_eye_calib.py` | Kalibracja reka-oko kamery na ramieniu (ArUco + AX=XB w numpy) -> `camera_on_arm.json`. | | `tools/arm_play.py` | Odtworzenie jednego nagranego ruchu (do testu bez calej petli). | diff --git a/docs/SETUP.md b/docs/SETUP.md index 702498b..6192a82 100644 --- a/docs/SETUP.md +++ b/docs/SETUP.md @@ -225,6 +225,10 @@ ktorych w repo nie ma). Skrypty maja w srodku port `/dev/tty.usbmodem...`, ### Jak sie polaczyc z Pi +> Aktualizacja 2026-09-26: WiFi na Pi dziala (hotspot iPhone, Maximize +> Compatibility ON). Aktualny poradnik polaczenia i odpalania panelu: +> `docs/PANEL.md`. Ponizej opis kabla ethernet (dalej dziala jako zapas). + - Kabel ethernet laptop<->Pi (adapter USB-Ethernet w laptopie). Pi ma **statyczne IP `192.168.137.5`** (ustawione przez `nmcli` na "Wired connection 1"), laptop `192.168.137.1` (Windows ICS - Internet diff --git a/docs/STATUS.md b/docs/STATUS.md index a0a287b..37536a0 100644 --- a/docs/STATUS.md +++ b/docs/STATUS.md @@ -18,6 +18,9 @@ zadania i przypisania na tablicy Projects (link nizej). Czego nie ma tutaj albo logi na zywo i ZIP (`/logs.zip`, z `pinecone_log.csv`), zdrowie Pi, zdarzenia, "co zbudowalismy" (liczby z repo). `/show` = tryb pokazu na projektor (bez sterowania, NIE trzyma heartbeatu jazdy). Sprawdzony na laptopie w `--demo` (ramie-atrapa, kamera z symulatora): liczenie, restart uslug, logi, telefon 375 px. 20 nowych testow, 164 zielone. +- Podglad kamery OBOK lerobot: `tools/cam_preview.py lerobot-record|lerobot-rollout ...` (port 8081) - serwer MJPEG + w tym samym procesie, podglada `read_latest()` kamery lerobot (nie zajmuje jej drugi raz). `act_pick.py` uzywa go + domyslnie. Testy 6 + sprawdzone z prawdziwym lerobot `OpenCVCamera` na laptopie; NA PI z RealSense NIE sprawdzone. - Kamera D415: podglad `rs_mjpeg_server.py` (glebia 424x240 -> mniejszy MinZ, bliski dywan ma ciagla glebie), kolory glebi jak w RealSense Viewer (`--colormap viewer`, domyslnie; stara skala liniowa: `--colormap fixed`), detekcja szyszek z glebi (`scan_cones.py`, rozrzut < 2 mm). - Nowy stos `pinecone_bot` (PR #14 + poprawki PR #16): symulacja na laptopie zbiera 5/5 szyszek, 66 testow zielonych. Ramie odtwarza nagrane punkty, baza ustawia szyszke z obrazu, maszyna stanow, szukanie pasami. Bez IK, bez ML. @@ -42,6 +45,7 @@ zadania i przypisania na tablicy Projects (link nizej). Czego nie ma tutaj albo (speed/steer/sekundy, bez limitu z suwaka) / ramie (ruch z motions/) / czekaj, "TEST TEGO KROKU", szkic w przegladarce, zapis do `sequences/.json`, odtwarzanie w trybie "sequence" (`pinecone_bot/sequence.py`, 12 testow; STOP/failsafe/zmiana trybu przerywa i zeruje jazde + STOP ramienia). Sprawdzone w przegladarce na atrapie ramienia (`--fake`) i bez Xiao; NIE na sprzecie. +- Polaczenie laptop -> Pi (hotspot albo kabel) i odpalenie obu paneli (:8000 jazda, :8010 ramie): poradnik `docs/PANEL.md`, sprawdzone 2026-09-26. Na Pi `pytest` 136 zielonych (bez `test_calibrate_target.py`), `./arm.sh status` czyta 6 przegubow. - `motions/grasp_mid.json`: chwyt z `demo2_fixed.csv` (aktualna kalibracja). `home.json`, `drop_box.json` (placeholder). - `tools/record_motion.py` (commit 40a75aa): ciagle nagranie ruchu ramienia prowadzonego reka (bez jazdy do HOME, kamera na ramieniu), probki 10 Hz, 'q'+Enter konczy i oddaje torque, zapis `motions/.json` (waypointy co 0.25 s w tempie prowadzenia, pierwszy z dojazdem 1.5 s); odtwarzanie `tools/arm_play.py --motion `. Testy `tests/test_record_motion.py` (3). Zastapilo dla operatora `tools/record_waypoints.py` (punkt po punkcie, uciazliwe) i legacy `record_demo.py` (jazda do HOME, stala liczba sekund). @@ -49,6 +53,10 @@ zadania i przypisania na tablicy Projects (link nizej). Czego nie ma tutaj albo - Jog XYZ w panelu ramienia (`pinecone_bot/kinematics.py`, sekcja JOG XYZ): testy + atrapa, NIE sprawdzony na ramieniu. Najpierw ZERO URDF (ramie prosto poziomo do przodu), potem sprawdzic, czy GORA jedzie w gore (inaczej `arm.urdf_sign`). - 2026-09-26: ROBOT WJECHAL W RAMIE I JE USZKODZIL (panel jazdy po hotspocie z duzym opoznieniem). Stan ramienia do oceny, serwa nie zasilac przed ogledzinami. Pi przestal odpowiadac (ping 100% strat). +- `tools/drive_calib.py` (branch pawel/drive-calib, draft PR): kalibracja jazdy bez miarki - droga z glebi RealSense + do sciany, kat z phyphox, pytanie l/p -> xiao_pwm_*/xiao_steer_*, steer_sign, heading.sign. Testy 9, NA PI NIE + uruchomione. Prawdopodobnie dubluje `base_test.py --measure` (pawel/base-calibration), `turn_loop.py` + (frane/gyro-rate-loop) i jazde po mapie `zygzak.py` (frane/mapa-d435) - przed uzyciem zdecydowac, co zostaje. - `pinecone_bot` NIE JECHAL jeszcze na sprzecie. Wszystko ponizej to pierwsze uruchomienie (docs/RUNBOOK.md). - Nowy prog HSV (branch, commit 2ba7fc9) NIE jest jeszcze wpisany na Pi - do wypchniecia razem z blokada AWB/ekspozycji (`lock_auto`, sekcja "camera" configu, PR #30), ktora jest na masterze, ale NIE na Pi (`pinecone_bot/camera.py`/`config.py` na Pi sa starsze). Reka w kadrze ma podobny odcien co szyszka (bloby 9000-31500 px, szyszka max ~4000 px) - `max_area_px` 40000 tego nie odrzuca, warto zmniejszyc do ~8000 (niezmienione). @@ -92,9 +100,16 @@ zadania i przypisania na tablicy Projects (link nizej). Czego nie ma tutaj albo Laptop GOTOWY: torch 2.11 cu128 (CUDA na RTX 3070), lerobot 0.6.1 [phone,feetech,async], `so101.json` skopiowany z Pi. Pi GOTOWE: lerobot 0.6.1 + placo (IK), hebi-py/teleop (telefon), grpcio (async client), torchvision; importy i IK na URDF sprawdzone. `~/hackaton/examples/phone_to_so100/` na Pi (skrypty v0.6.1 + SO101 z STL, poza gitem). Teleop telefonem, druga kamera, - nagranie datasetu: nic nie odpalone. JEST leader SO-101 (sala 435 D) -> sciezka glowna to `lerobot-record` z leaderem - (SETUP.md "Wariant z leaderem"), telefon/placo tylko awaryjnie. Leader bez kalibracji; nowa regula udev `robot-leader` - (po serialu CH343) w `deploy/99-robot.rules`, WGRANA na Pi. + JEST leader SO-101 -> `lerobot-record` z leaderem (SETUP.md "Wariant z leaderem"), telefon/placo tylko awaryjnie. + 2026-09-27: leader na Pi jako `/dev/robot-leader` (w miejscu kabla hovera - Pi nie ma wolnego USB), kalibracja leadera + = KOPIA `so101.json` followera (decyzja frane) + gripper przez `tools/calibrate_joint.py`; czesc osi leadera odwrocona + (dane dla ACT i tak poprawne: akcja = cel followera). Kamera D435 serial 030522070668 (nie 105422060821 z SETUP). + Podzial na 2 etapy: ACT uczy sie TYLKO chwytu (HOME -> szyszka -> zamkniecie -> uniesienie, 15 s), wrzut do sloika + robi nagrany `motions/drop_box.json` (tylko na Pi: tam i z powrotem, 15 s). Datasety na Pi `~/datasets/`: + `so101_grasp` (7 ep.), `so101_grasp2` (50 ep., gotowy). Na Pi doinstalowane `lerobot[dataset]` z torch cpu przypietym + (override), spadek napiecia uszkodzil `pyarrow`/`av` - przeinstalowane. `tools/act_pick.py` = etap 1 (`lerobot-rollout` + z ACT, torque zostaje) + etap 2 (`arm_play drop_box --home-after`), testy 7, podglad kamery :8081; NIE uruchomiony (wagi sa, patrz nizej). + Laptop pawel120 (Intel Arc, bez NVIDIA, bez venv) NIE nadaje sie do treningu - trening na laptopie z RTX 3070. - ACT (2026-09-27 po poludniu): dataset `datasets/so101_grasp2` (50 epizodow, 22451 klatek, kamera wrist D435 serial 030522070668, leader skalibrowany 12:30) NAGRANY na Pi przez druga sesje i wrzucony na master (wideo w Git LFS). Trening na laptopie (`tools/train_win.py`, batch 8, AMP, ~3 kroki/s po podpieciu zasilacza): loss 26 -> 1.24 przy @@ -105,6 +120,10 @@ zadania i przypisania na tablicy Projects (link nizej). Czego nie ma tutaj albo `tools/hand_eye_calib.py` (marker/collect/solve/predict, AX=XB) gotowe z testami (28), instrukcja `docs/ARM_FRAMES.md`. NIE uruchomione na sprzecie - do zrobienia przez sesje przy Pi. To warunek wstepny dla IK/GraspGenX. +- 2026-09-26 wieczor: chwytanie samym ramieniem (baza stoi), instrukcja: `docs/LIVE_GRASP.md`, kod `tools/live_grasp/`. + Dziala: szyszki z glebi w HOME, nagrywanie ruchu reka, odtworzenie cyklu chwyt -> sloik. Nie dziala: autonomiczny chwyt + (model z 8 niepewnych probek, blad ok. 6 cm). Nowy HOME w repo, NIE wgrany na Pi. + ## Nastepne 3 kroki (w tej kolejnosci) Dwa tory rownolegle. Tor mapa + zygzak (frane/mapa-d435, `docs/MAPA.md`): @@ -114,8 +133,12 @@ Dwa tory rownolegle. Tor mapa + zygzak (frane/mapa-d435, `docs/MAPA.md`): 3. Poza ramienia "szukaj" (kamera 38 st w dol) i szyszki w zygzaku (detektor + podjazd z `brain.py`); merge frane/mapa-d435. Tor ACT / ramie (master): +A. (ACT) wagi `pretrained_model` na Pi, `python tools/act_pick.py --policy --skip-drop` + z wylacznikiem w rece, potem bez `--skip-drop`. 0. (sesja przy Pi, rownolegle z ACT) `docs/ARM_FRAMES.md`: `tools/frame_check.py` z wylacznikiem, potem `tools/hand_eye_calib.py collect/solve` z markerem ArUco -> `camera_on_arm.json` do repo. +0. Chwytanie samym ramieniem: czyste uczenie (1 szyszka naraz, jeden styl chwytu, 12 pozycji), `fit.py`, test `pick.py` + - dokladne kroki w `docs/LIVE_GRASP.md`. Swiatlo w pokoju konieczne. 1. Wpisac nowy prog HSV na Pi (albo push z brancha po merge) i sprawdzic na zywo; odczytac limity EEPROM barku, potem `tools/arm_play.py --motion grasp_near` z reka na wylaczniku. 2. Nagrac `drop_box` (`tools/record_motion.py --name drop_box`), dopisac chwyty do `cfg.grasps`, `tools/calibrate_target.py`. 3. `tools/base_test.py`, `tools/phyphox_check.py` (znak kursu), potem `python -m pinecone_bot.main --dry-run --heading phyphox`, potem `--real` z wylacznikiem w rece. diff --git a/motions/home.json b/motions/home.json index 78dab06..c3513a9 100644 --- a/motions/home.json +++ b/motions/home.json @@ -1,10 +1,17 @@ { "name": "home", - "note": "HOME_POSE z arm_control.py (kalibracja po fix_shoulder_offset.py, 2026-09-25). Po kazdej zmianie kalibracji serw przepisac razem z arm_control.HOME_POSE.", + "note": "HOME pod kamere na ramieniu: odczyt ./arm.sh status 2026-09-26 (poza ustawiona recznie). Po kazdej zmianie przepisac razem z arm_control.HOME_POSE i pinecone_bot/arm.py HOME_POSE.", "waypoints": [ { "label": "home", - "pose": {"shoulder_pan": 1.27, "shoulder_lift": -85.05, "elbow_flex": 99.0, "wrist_flex": -102.11, "wrist_roll": 89.1, "gripper": 1.69}, + "pose": { + "shoulder_pan": -5.45, + "shoulder_lift": 88.92, + "elbow_flex": 7.56, + "wrist_flex": -87.87, + "wrist_roll": 88.88, + "gripper": 41.06 + }, "seconds": 2.0 } ] diff --git a/motions/tables/grasp_table.json b/motions/tables/grasp_table.json new file mode 100644 index 0000000..59614d8 --- /dev/null +++ b/motions/tables/grasp_table.json @@ -0,0 +1,26 @@ +{ + "note": "tools/teach_grasp.py: piksel szyszki w HOME -> ramie nad szyszka (pre) i chwyt (grasp). Zmiana HOME, montazu kamery albo kalibracji serw = uczyc od nowa.", + "samples": [ + { + "px": 364, + "py": 229, + "pre": { + "shoulder_pan": -4.31, + "shoulder_lift": 86.64, + "elbow_flex": -3.87, + "wrist_flex": -87.87, + "wrist_roll": 88.97, + "gripper": 41.06 + }, + "grasp": { + "shoulder_pan": -4.4, + "shoulder_lift": 89.1, + "elbow_flex": -13.36, + "wrist_flex": -65.54, + "wrist_roll": 88.09, + "gripper": 41.0 + }, + "note": "srodkowa, uczenie 2026-09-26" + } + ] +} diff --git a/pinecone_bot/arm.py b/pinecone_bot/arm.py index 8eaf48f..3d82e08 100644 --- a/pinecone_bot/arm.py +++ b/pinecone_bot/arm.py @@ -49,12 +49,12 @@ # WaypointArm.home() uzywa arm_control.HOME_POSE (zrodlo prawdy); ta kopia # sluzy tylko do motions/home.json i do dry-run bez lerobot. HOME_POSE = { - "shoulder_pan": 1.27, - "shoulder_lift": -85.05, - "elbow_flex": 99.0, - "wrist_flex": -102.11, - "wrist_roll": 89.10, - "gripper": 1.69, + "shoulder_pan": -5.45, + "shoulder_lift": 88.92, + "elbow_flex": 7.56, + "wrist_flex": -87.87, + "wrist_roll": 88.88, + "gripper": 41.06, } GRIPPER_OPEN = 100.0 # arm_control: gripper 0 = zamkniety, 100 = otwarty @@ -333,7 +333,10 @@ def _verify(self, wp: Waypoint, skip_gripper: bool) -> dict | None: # --- interfejs --------------------------------------------------------- def replay(self, name: str) -> bool | None: - motion = load_motion(self.cfg.arm.motions_dir, name) + return self.play(load_motion(self.cfg.arm.motions_dir, name)) + + def play(self, motion: Motion) -> bool | None: + """Jak replay, ale ruch z pamieci (np. chwyt z tabeli uczenia, pinecone_bot/grasp_table.py).""" result: bool | None = None log.info("replay '%s': %d waypointow, %.1f s", motion.name, len(motion.waypoints), motion.total_seconds) for wp in motion.waypoints: diff --git a/pinecone_bot/grasp_table.py b/pinecone_bot/grasp_table.py new file mode 100644 index 0000000..b7efd53 --- /dev/null +++ b/pinecone_bot/grasp_table.py @@ -0,0 +1,107 @@ +"""Tabela chwytow z uczenia: miejsce szyszki na obrazie (w HOME) -> nagrane pozycje ramienia. + +Samo ramie, baza stoi. Kamera siedzi na ramieniu, wiec obraz ma sens tylko w jednej pozycji: +detekcja zawsze w HOME. Bez IK i bez kalibracji kamera-ramie: przy uczeniu czlowiek kladzie +szyszke, program zapisuje jej piksel, a czlowiek ustawia ramie reka nad szyszka ("pre") +i w pozycji chwytu ("grasp"). Przy zbieraniu bierzemy probke najblizsza w pikselach +(tools/teach_grasp.py uczy, tools/pick_loop.py zbiera). + +Plik: motions/tables/grasp_table.json (ASCII); podkatalog, bo motions/*.json to same ruchy. +""" +from __future__ import annotations + +import json +import math +import os +from dataclasses import dataclass + +from .arm import GRIPPER_OPEN, JOINT_NAMES, Motion, Waypoint + +TABLE_NAME = "grasp_table.json" + + +@dataclass +class GraspSample: + px: float # srodek szyszki na obrazie w HOME (kolumna) + py: float # wiersz + pre: dict # ramie nad szyszka (6 przegubow) + grasp: dict # szczeki wokol szyszki (6 przegubow) + note: str = "" + + +def table_path(motions_dir: str) -> str: + return os.path.join(motions_dir, "tables", TABLE_NAME) + + +def _pose(raw, where: str) -> dict: + if not isinstance(raw, dict) or any(j not in raw for j in JOINT_NAMES): + raise ValueError(f"{where}: pozycja musi miec przeguby {JOINT_NAMES}") + return {j: float(raw[j]) for j in JOINT_NAMES} + + +def load_table(path: str) -> list: + """Brak pliku = pusta tabela.""" + if not os.path.exists(path): + return [] + with open(path, encoding="ascii") as fh: + data = json.load(fh) + samples = [] + for i, raw in enumerate(data.get("samples", [])): + samples.append(GraspSample( + px=float(raw["px"]), py=float(raw["py"]), + pre=_pose(raw.get("pre"), f"{path}: probka {i} pre"), + grasp=_pose(raw.get("grasp"), f"{path}: probka {i} grasp"), + note=str(raw.get("note", "")), + )) + return samples + + +def save_table(path: str, samples: list) -> None: + data = { + "note": "tools/teach_grasp.py: piksel szyszki w HOME -> ramie nad szyszka (pre) i chwyt (grasp). " + "Zmiana HOME, montazu kamery albo kalibracji serw = uczyc od nowa.", + "samples": [ + {"px": round(s.px, 1), "py": round(s.py, 1), + "pre": {j: round(s.pre[j], 2) for j in JOINT_NAMES}, + "grasp": {j: round(s.grasp[j], 2) for j in JOINT_NAMES}, + "note": s.note} + for s in samples + ], + } + os.makedirs(os.path.dirname(path) or ".", exist_ok=True) + tmp = path + ".tmp" + with open(tmp, "w", encoding="ascii") as fh: + json.dump(data, fh, indent=2) + fh.write("\n") + os.replace(tmp, path) + + +def pick_detection(dets: list): + """Najwieksza cala (nie ucieta krawedzia) szyszka albo None.""" + whole = [d for d in dets if not d.partial] + return max(whole, key=lambda d: d.area) if whole else None + + +def nearest_sample(samples: list, px: float, py: float, max_px: float): + """(probka, odleglosc w px) najblizsza (px, py) albo None, gdy zadna nie jest blizej niz max_px.""" + best = None + for s in samples: + d = math.hypot(s.px - px, s.py - py) + if d <= max_px and (best is None or d < best[1]): + best = (s, d) + return best + + +def grasp_motion(sample: GraspSample, squeeze: float = 0.0, slow: float = 1.0) -> Motion: + """Nad szyszke z otwartym chwytakiem -> w dol -> zacisk (sprawdzany) -> w gore. HOME robi wolajacy. + + slow > 1 wydluza wszystkie czasy (pierwsze proby). + """ + opened = lambda pose: {**pose, "gripper": GRIPPER_OPEN} # noqa: E731 + closed = lambda pose: {**pose, "gripper": float(squeeze)} # noqa: E731 + return Motion(name="pick", note=sample.note, waypoints=[ + Waypoint("nad szyszka", opened(sample.pre), 2.0 * slow), + Waypoint("w dol", opened(sample.grasp), 1.5 * slow), + Waypoint("zacisk", closed(sample.grasp), 1.0 * slow, check_gripper=True), + Waypoint("w gore", closed(sample.pre), 1.5 * slow), + ]) diff --git a/tests/test_act_pick.py b/tests/test_act_pick.py new file mode 100644 index 0000000..73b5fe8 --- /dev/null +++ b/tests/test_act_pick.py @@ -0,0 +1,71 @@ +"""Testy tools/act_pick.py - skladanie komend etapow (bez sprzetu i bez lerobot).""" +from __future__ import annotations + +import os +import sys +from types import SimpleNamespace + +sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "tools")) + +from act_pick import TASK, drop_cmd, main, rollout_cmd, with_preview # noqa: E402 + + +def test_rollout_keeps_torque_and_matches_dataset_camera(): + cmd = rollout_cmd("/m/act", 12) + assert "--policy.path=/m/act" in cmd + assert "--robot.disable_torque_on_disconnect=false" in cmd + assert "--duration=12" in cmd + assert "--device=cpu" in cmd # Pi bez CUDA, nawet gdy wagi uczone na cuda + assert f"--task={TASK}" in cmd + cams = next(c for c in cmd if c.startswith("--robot.cameras=")) + assert "wrist:" in cams and "width: 640" in cams and "height: 480" in cams + + +def test_rollout_runs_through_camera_preview_by_default(capsys): + assert main(["--policy", "p", "--dry-run", "--skip-drop"], run=None) == 0 + line = capsys.readouterr().out.splitlines()[0] + assert "tools/cam_preview.py --preview-port=8081 lerobot-rollout --strategy.type=base" in line + + +def test_with_preview_keeps_lerobot_args_and_can_be_disabled(): + cmd = rollout_cmd("/m/act", 12) + wrapped = with_preview(cmd, 9000) + assert wrapped[1:4] == ["tools/cam_preview.py", "--preview-port=9000", "lerobot-rollout"] + assert wrapped[4:] == cmd[1:] + assert with_preview(cmd, 0) == cmd + + +def test_drop_goes_home_after(): + cmd = drop_cmd(port="/dev/x") + assert cmd[1:] == ["tools/arm_play.py", "--motion", "drop_box", "--port", "/dev/x", "--home-after"] + + +def test_main_runs_stages_in_order_and_repeats(): + calls = [] + + def run(cmd, cwd=None): + calls.append("lerobot-rollout" if "lerobot-rollout" in cmd else cmd[1]) + return SimpleNamespace(returncode=0) + + assert main(["--policy", "p", "--repeat", "2"], run=run) == 0 + assert calls == ["lerobot-rollout", "tools/arm_play.py", "lerobot-rollout", "tools/arm_play.py"] + + +def test_main_stops_on_failed_grasp_stage(): + calls = [] + + def run(cmd, cwd=None): + calls.append(cmd) + return SimpleNamespace(returncode=3) + + assert main(["--policy", "p"], run=run) == 3 + assert len(calls) == 1 # bez wrzutu, gdy rollout padl + + +def test_dry_run_and_skip_drop_run_nothing(capsys): + def run(cmd, cwd=None): + raise AssertionError("dry-run nie moze nic uruchamiac") + + assert main(["--policy", "p", "--dry-run", "--skip-drop"], run=run) == 0 + out = capsys.readouterr().out + assert "lerobot-rollout" in out and "arm_play" not in out diff --git a/tests/test_arm.py b/tests/test_arm.py index 304ae58..5133cfd 100644 --- a/tests/test_arm.py +++ b/tests/test_arm.py @@ -130,6 +130,7 @@ def test_all_motion_files_load(): fh.read().decode("ascii") # regula zespolu: tylko ASCII +@pytest.mark.xfail(reason="grasp_mid nagrany pod stary HOME (przed kamera na ramieniu); nagrac na nowo pod motions/home.json", strict=True) def test_grasp_mid_is_a_grasp(): # Tresc ruchu zmienia sie przy kazdym nagraniu, wiec tu tylko to, co musi # zachodzic zawsze: start i koniec w HOME_POSE, dokladnie jeden punkt diff --git a/tests/test_arm_panel.py b/tests/test_arm_panel.py index 03730fe..8a9cdb1 100644 --- a/tests/test_arm_panel.py +++ b/tests/test_arm_panel.py @@ -222,7 +222,7 @@ def test_motion_list_and_replay(): def test_stop_interrupts_motion_and_clears_queue(): panel, arm, clock = make_panel() - panel.submit({"cmd": "jog", "joint": "shoulder_lift", "step": 10}) + panel.submit({"cmd": "jog", "joint": "shoulder_lift", "step": -10}) panel.submit({"cmd": "jog", "joint": "elbow_flex", "step": 10}) start = arm.pose["shoulder_lift"] ticks = {"n": 0} @@ -237,12 +237,12 @@ def press_stop(): clock.hook = None assert "przerwane" in panel.last_result moved = arm.pose["shoulder_lift"] - start - assert 0 < moved < 10 + assert -10 < moved < 0 # w dol: HOME jest blisko gornej granicy shoulder_lift assert not panel.process_one(), "STOP musi wyczyscic kolejke" # po STOP nowe komendy dzialaja i startuja od miejsca zatrzymania - assert panel.submit({"cmd": "jog", "joint": "shoulder_lift", "step": 1})[0] + assert panel.submit({"cmd": "jog", "joint": "shoulder_lift", "step": -1})[0] panel.process_one() - assert arm.pose["shoulder_lift"] == pytest.approx(start + moved + 1) + assert arm.pose["shoulder_lift"] == pytest.approx(start + moved - 1) def test_stop_interrupts_waypoint_replay(): @@ -501,6 +501,8 @@ def test_jog_back_into_range_allowed_when_slightly_outside(): panel.process_one() assert panel.last_error is None assert lo <= arm.pose["wrist_roll"] <= lo + 5.0 + + def test_default_speed_is_halved(): # 2026-09-26: po wjechaniu robota w ramie predkosc panelu zmniejszona o polowe from pinecone_bot.arm_panel import DEFAULT_MAX_STEP diff --git a/tests/test_calibrate_joint.py b/tests/test_calibrate_joint.py new file mode 100644 index 0000000..4b91e2c --- /dev/null +++ b/tests/test_calibrate_joint.py @@ -0,0 +1,100 @@ +"""Testy matematyki tools/calibrate_joint.py (bez lerobot i bez sprzetu).""" +from __future__ import annotations + +import json +import os +import sys + +import pytest + +REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +sys.path.insert(0, os.path.join(REPO_ROOT, "tools")) + +import calibrate_joint as cj # noqa: E402 + + +def test_unwrap_crosses_encoder_zero(): + assert cj.unwrap([4090, 4095, 3, 10]) == [4090, 4095, 4099, 4106] + assert cj.unwrap([5, 0, 4093]) == [5, 0, -3] + + +def test_wrap_offset_range(): + assert cj.wrap_offset(-2119) == 1977 + assert cj.wrap_offset(2047) == 2047 + assert cj.wrap_offset(2048) == -2048 + assert -2048 <= cj.wrap_offset(12345) <= 2047 + + +def test_propose_reproduces_2026_09_25_shoulder_fix(): + # Stary offset -701, Present przechodzil przez zero: -362..1621 (4095 -> 0). + # Recznie policzona poprawka (legacy/arm_recordings/fix_shoulder_offset.py): offset 1977, + # zakres 1056..3039 (potem z marginesem 1006..3089). + sweep = list(range(4096 - 362, 4096, 7)) + list(range(0, 1622, 7)) + new = cj.propose(sweep, old_offset=-701) + assert new["homing_offset"] == pytest.approx(1977, abs=2) + assert new["range_min"] == pytest.approx(1056, abs=8) + assert new["range_max"] == pytest.approx(3039, abs=8) + assert (new["range_min"] + new["range_max"]) / 2 == pytest.approx(cj.CENTER, abs=2) + + +def test_propose_centered_range_is_stable(): + # zakres juz wysrodkowany przy offsecie 1977 -> offset i limity prawie bez zmian + new = cj.propose(list(range(1006, 3090, 5)) + [3089], old_offset=1977) + assert new["homing_offset"] == pytest.approx(1977, abs=1) + assert new["range_min"] == pytest.approx(1006, abs=2) + assert new["range_max"] == pytest.approx(3089, abs=2) + + +def test_propose_rejects_tiny_or_empty_sweep(): + with pytest.raises(ValueError): + cj.propose([], old_offset=0) + with pytest.raises(ValueError): + cj.propose([2000, 2010, 2020], old_offset=0) + + +def test_degrees_like_lerobot(): + cal = {"range_min": 1006, "range_max": 3089} + assert cj.deg_half_range(cal) == pytest.approx(91.6, abs=0.1) + assert cj.present_to_deg(2047.5, cal) == pytest.approx(0.0) + + +def test_save_joint_touches_one_entry_and_backs_up(tmp_path): + path = tmp_path / "so101.json" + calib = { + "shoulder_lift": {"id": 2, "drive_mode": 0, "homing_offset": 1977, "range_min": 1006, "range_max": 3089}, + "elbow_flex": {"id": 3, "drive_mode": 0, "homing_offset": 1159, "range_min": 1168, "range_max": 3423}, + } + path.write_text(json.dumps(calib)) + backup = cj.save_joint(str(path), "shoulder_lift", {"homing_offset": 100, "range_min": 1000, "range_max": 3000, "span": 2000}) + new = json.loads(path.read_text()) + assert new["shoulder_lift"] == {"id": 2, "drive_mode": 0, "homing_offset": 100, "range_min": 1000, "range_max": 3000} + assert new["elbow_flex"] == calib["elbow_flex"] + assert json.loads(open(backup).read()) == calib + + +def test_propose_many_skips_joint_that_did_not_move(): + samples = {"shoulder_lift": list(range(1006, 3090, 5)), "gripper": [2000, 2005, 2010]} + proposals, errors = cj.propose_many(samples, {"shoulder_lift": 1977, "gripper": 0}) + assert set(proposals) == {"shoulder_lift"} + assert set(errors) == {"gripper"} + + +def test_save_joints_writes_many_with_one_backup(tmp_path): + path = tmp_path / "so101.json" + calib = { + "shoulder_lift": {"id": 2, "drive_mode": 0, "homing_offset": 1977, "range_min": 1006, "range_max": 3089}, + "elbow_flex": {"id": 3, "drive_mode": 0, "homing_offset": 1159, "range_min": 1168, "range_max": 3423}, + "gripper": {"id": 6, "drive_mode": 0, "homing_offset": 0, "range_min": 2000, "range_max": 3000}, + } + path.write_text(json.dumps(calib)) + news = { + "shoulder_lift": {"homing_offset": 100, "range_min": 1000, "range_max": 3000, "span": 2000}, + "elbow_flex": {"homing_offset": -50, "range_min": 1100, "range_max": 2900, "span": 1800}, + } + backup = cj.save_joints(str(path), news) + new = json.loads(path.read_text()) + assert new["shoulder_lift"]["homing_offset"] == 100 + assert new["elbow_flex"] == {"id": 3, "drive_mode": 0, "homing_offset": -50, "range_min": 1100, "range_max": 2900} + assert new["gripper"] == calib["gripper"] + assert json.loads(open(backup).read()) == calib + assert len(list(tmp_path.glob("so101.json.bak-*"))) == 1 diff --git a/tests/test_cam_preview.py b/tests/test_cam_preview.py new file mode 100644 index 0000000..e1b6d58 --- /dev/null +++ b/tests/test_cam_preview.py @@ -0,0 +1,112 @@ +"""Testy tools/cam_preview.py - podglad MJPEG w procesie lerobot (atrapa kamery, bez lerobot i sprzetu).""" +from __future__ import annotations + +import os +import sys +import urllib.request + +import cv2 +import numpy as np +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "tools")) + +import cam_preview # noqa: E402 + + +class FakeCam: + """Jak kamera lerobot: read_latest() podglada bufor, nie zabiera klatki.""" + + def __init__(self, frame=None, color_mode="rgb"): + self.frame = frame + self.color_mode = color_mode + self.is_connected = True + self.reads = 0 + + def read_latest(self, max_age_ms=500): + self.reads += 1 + if self.frame is None: + raise RuntimeError("has not captured any frames yet") + return self.frame + + def __str__(self): + return "FakeCam(wrist)" + + +@pytest.fixture(autouse=True) +def clean_registry(): + cam_preview.CAMERAS.clear() + yield + cam_preview.CAMERAS.clear() + + +def red_rgb(): + frame = np.zeros((48, 64, 3), np.uint8) + frame[..., 0] = 255 # R w RGB + return frame + + +def test_grab_jpeg_converts_lerobot_rgb_to_bgr(): + jpg = cam_preview.grab_jpeg(FakeCam(red_rgb())) + img = cv2.imdecode(np.frombuffer(jpg, np.uint8), cv2.IMREAD_COLOR) + b, g, r = img[24, 32] + assert r > 200 and b < 50 # czerwony zostal czerwony, nie niebieski + + +def test_grab_jpeg_returns_none_when_camera_not_ready(): + assert cam_preview.grab_jpeg(FakeCam(None)) is None + + +@pytest.fixture +def server(): + srv = cam_preview.start_server("127.0.0.1", 0, fps=50) + yield f"http://127.0.0.1:{srv.server_address[1]}" + srv.shutdown() + srv.server_close() + + +def test_index_lists_registered_cameras(server): + html = urllib.request.urlopen(server + "/", timeout=5).read().decode() + assert "Brak kamer" in html + cam_preview.register(FakeCam(red_rgb())) + html = urllib.request.urlopen(server + "/", timeout=5).read().decode() + assert "/cam/0.mjpg" in html and "FakeCam(wrist)" in html + + +def test_snapshot_and_stream_serve_jpeg(server): + cam = FakeCam(red_rgb()) + cam_preview.register(cam) + snap = urllib.request.urlopen(server + "/cam/0.jpg", timeout=5) + assert snap.headers["Content-Type"] == "image/jpeg" + assert snap.read()[:2] == b"\xff\xd8" + + stream = urllib.request.urlopen(server + "/cam/0.mjpg", timeout=5) + assert stream.headers["Content-Type"].startswith("multipart/x-mixed-replace") + chunk = stream.read(200) + assert chunk.startswith(b"--frame") and b"image/jpeg" in chunk + stream.close() + + +def test_unknown_camera_is_404(server): + with pytest.raises(urllib.error.HTTPError) as err: + urllib.request.urlopen(server + "/cam/3.jpg", timeout=5) + assert err.value.code == 404 + + +def test_main_runs_entry_in_same_process_with_its_args(): + seen = {} + + def entry(): + seen["argv"] = list(sys.argv) + return None + + old_argv = sys.argv + try: + rc = cam_preview.main(["--preview-port", "0", "--preview-host", "127.0.0.1", + "lerobot-rollout", "--robot.id=so101", "--fps=30"], + resolve=lambda name: entry, hook=lambda: seen.setdefault("hook", True)) + finally: + sys.argv = old_argv + assert rc == 0 + assert seen["hook"] is True + assert seen["argv"] == ["lerobot-rollout", "--robot.id=so101", "--fps=30"] diff --git a/tests/test_drive_calib.py b/tests/test_drive_calib.py new file mode 100644 index 0000000..7b721a6 --- /dev/null +++ b/tests/test_drive_calib.py @@ -0,0 +1,128 @@ +"""Testy tools/drive_calib.py - atrapa swiata: robot przed sciana, Xiao z tarciem, zyroskop phyphox.""" +from __future__ import annotations + +import math +import os +import sys + +import numpy as np +import pytest + +sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "tools")) + +from drive_calib import Calibrator, apply, fit_pwm, signs_from_turn, wall_distance # noqa: E402 +from pinecone_bot.base import XIAO_SPEED_LIMIT, XIAO_STEER_LIMIT, xiao_map # noqa: E402 +from pinecone_bot.config import Config # noqa: E402 + +# "prawdziwe" podwozie: pwm = P0 + S * v (ponizej P0 kola stoja) +P0, S = 100, 800.0 +STEER_P0, STEER_S = 50, 600.0 + + +class World: + def __init__(self, cfg, wall_m=2.0, steer_inverted=False, phone_face_down=False): + self.cfg, self.wall_m = cfg, wall_m + self.steer_inverted, self.phone_face_down = steer_inverted, phone_face_down + self.x = self.yaw = 0.0 + self.v = self.w = 0.0 + self.last_turn_left = None + + # base + def set_speed(self, v, w): + c, b = self.cfg.control, self.cfg.base + pwm = xiao_map(v, c.v_max, b.xiao_pwm_min, b.xiao_pwm_max, XIAO_SPEED_LIMIT) + spwm = xiao_map(w, c.w_max, b.xiao_steer_min, b.xiao_steer_max, XIAO_STEER_LIMIT) + self.v = math.copysign(max(0.0, abs(pwm) - P0) / S, pwm) + self.w = math.copysign(max(0.0, abs(spwm) - STEER_P0) / STEER_S, spwm) * (-1 if self.steer_inverted else 1) + if self.w: + self.last_turn_left = self.w > 0 + + def stop(self): + self.v = self.w = 0.0 + + def sleep(self, dt): + self.x += self.v * dt + self.yaw += self.w * dt + + # gyro: telefon ekranem w dol odwraca znak + def yaw_reading(self): + return self.yaw * (-1 if self.phone_face_down else 1) * self.cfg.heading.sign + + def depth(self): + return np.full((48, 64), self.wall_m - self.x, np.float32) + + +def calibrate(world, speeds=(0.10, 0.20), rates=(0.3, 0.5)): + gyro = type("G", (), {"yaw": lambda _self: world.yaw_reading()})() + cal = Calibrator(world.cfg, world, gyro, world.depth, ask=lambda _p: world.last_turn_left, + sleep=world.sleep, log=lambda *_: None) + return cal.run(list(speeds), 2.0, list(rates), 2.0) + + +def test_good_hardware_keeps_signs_and_recovers_pwm_line(): + cfg = Config() + world = World(cfg) + res = calibrate(world) + assert res["steer_sign"] == 1.0 and res["heading_sign"] == 1.0 + pmin, pmax = res["xiao_pwm"] + assert abs(pmin - P0) <= 3 and abs(pmax - (P0 + S * cfg.control.v_max)) <= 3 + smin, smax = res["xiao_steer"] + assert abs(smin - STEER_P0) <= 3 and abs(smax - (STEER_P0 + STEER_S * cfg.control.w_max)) <= 3 + assert abs(res["forward"][0]["drift_deg"]) < 0.1 + assert abs(world.yaw) < math.radians(15) # obroty na zmiane: robot wraca mniej wiecej do kierunku + + +def test_inverted_steering_and_face_down_phone_flip_both_signs(): + cfg = Config() + res = calibrate(World(cfg, steer_inverted=True, phone_face_down=True)) + assert res["steer_sign"] == -1.0 + assert res["heading_sign"] == -1.0 + + +def test_inverted_steering_with_good_phone_flips_only_steer(): + cfg = Config() + res = calibrate(World(cfg, steer_inverted=True)) + assert res["steer_sign"] == -1.0 and res["heading_sign"] == 1.0 + + +def test_signs_from_turn_table(): + assert signs_from_turn(0.3, +0.5, True, 1.0) == (1.0, 1.0) + assert signs_from_turn(0.3, -0.5, True, 1.0) == (1.0, -1.0) + assert signs_from_turn(0.3, -0.5, False, 1.0) == (-1.0, 1.0) + assert signs_from_turn(-0.3, +0.5, True, -1.0) == (-1.0, -1.0) + + +def test_wheels_not_moving_are_excluded_from_fit(): + cfg = Config() + res = calibrate(World(cfg), speeds=(0.001, 0.10, 0.20)) + assert res["forward"][0]["dist_m"] == 0.0 + assert res["xiao_pwm"] is not None # dopasowane z dwoch pozostalych + + +def test_fit_pwm_rejects_single_point_and_nonsense(): + assert fit_pwm([(150, 0.06)], 0.25) is None + assert fit_pwm([(150, 0.10), (200, 0.05)], 0.25) is None # wiecej PWM = wolniej + assert fit_pwm([(180, 0.10), (260, 0.20)], 0.25) == (100, 300) + + +def test_wall_distance_median_and_holes(): + d = np.full((48, 64), 1.5, np.float32) + assert wall_distance([d] * 5) == pytest.approx(1.5) + assert wall_distance([np.zeros((48, 64), np.float32)] * 5) is None + + +def test_too_close_to_wall_stops_before_driving(): + cfg = Config() + world = World(cfg, wall_m=0.5) + with pytest.raises(RuntimeError, match="za blisko"): + calibrate(world) + assert world.x == 0.0 + + +def test_apply_writes_only_measured_fields(): + cfg = Config() + res = {"steer_sign": -1.0, "heading_sign": None, "xiao_pwm": (95, 290), "xiao_steer": None} + changes = apply(cfg, res) + assert cfg.control.steer_sign == -1.0 and cfg.heading.sign == 1.0 + assert (cfg.base.xiao_pwm_min, cfg.base.xiao_pwm_max) == (95, 290) + assert len(changes) == 3 diff --git a/tests/test_grasp_table.py b/tests/test_grasp_table.py new file mode 100644 index 0000000..a526199 --- /dev/null +++ b/tests/test_grasp_table.py @@ -0,0 +1,91 @@ +"""Tabela chwytow z uczenia (pinecone_bot/grasp_table.py) i WaypointArm.play.""" +from __future__ import annotations + +import os +import sys + +import pytest + +REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +if REPO_ROOT not in sys.path: + sys.path.insert(0, REPO_ROOT) + +from pinecone_bot import arm as arm_mod # noqa: E402 +from pinecone_bot.arm import GRIPPER_OPEN, JOINT_NAMES # noqa: E402 +from pinecone_bot.detector import Detection # noqa: E402 +from pinecone_bot.grasp_table import ( # noqa: E402 + GraspSample, + grasp_motion, + load_table, + nearest_sample, + pick_detection, + save_table, +) +from test_arm import FakeArm, waypoint_arm # noqa: E402 (pytest dodaje tests/ do sys.path) + + +def pose(**kw) -> dict: + return {**dict(arm_mod.HOME_POSE), **kw} + + +def sample(px, py, lift=10.0) -> GraspSample: + return GraspSample(px=px, py=py, pre=pose(shoulder_lift=lift), grasp=pose(shoulder_lift=lift - 20), note=f"{px},{py}") + + +def test_table_roundtrip_is_ascii(tmp_path): + path = str(tmp_path / "grasp_table.json") + assert load_table(path) == [] + save_table(path, [sample(100, 200), sample(300, 250)]) + with open(path, "rb") as fh: + fh.read().decode("ascii") + loaded = load_table(path) + assert [(s.px, s.py) for s in loaded] == [(100, 200), (300, 250)] + assert set(loaded[0].grasp) == set(JOINT_NAMES) + + +def test_load_rejects_missing_joint(tmp_path): + path = tmp_path / "grasp_table.json" + path.write_text('{"samples": [{"px": 1, "py": 2, "pre": {"gripper": 1}, "grasp": {}}]}', encoding="ascii") + with pytest.raises(ValueError): + load_table(str(path)) + + +def test_nearest_sample_respects_max_px(): + table = [sample(100, 200), sample(300, 250)] + s, d = nearest_sample(table, 290, 240, max_px=80) + assert s.px == 300 and d == pytest.approx(14.14, abs=0.01) + assert nearest_sample(table, 600, 50, max_px=80) is None + assert nearest_sample([], 0, 0, max_px=80) is None + + +def test_pick_detection_skips_partial_and_takes_largest(): + small = Detection(px=10, py=10, area=100, bbox=(0, 0, 10, 10)) + big_cut = Detection(px=20, py=20, area=900, bbox=(0, 0, 30, 30), partial=True) + big = Detection(px=30, py=30, area=400, bbox=(0, 0, 20, 20)) + assert pick_detection([small, big_cut, big]) is big + assert pick_detection([big_cut]) is None + + +def test_grasp_motion_opens_above_checks_on_squeeze_and_lifts(): + m = grasp_motion(sample(100, 200), squeeze=0.0) + labels = [wp.label for wp in m.waypoints] + assert labels == ["nad szyszka", "w dol", "zacisk", "w gore"] + assert m.waypoints[0].pose["gripper"] == GRIPPER_OPEN + assert m.waypoints[1].pose["gripper"] == GRIPPER_OPEN + assert [wp.check_gripper for wp in m.waypoints] == [False, False, True, False] + assert m.waypoints[3].pose["gripper"] == 0.0 # trzyma szyszke w gore + assert grasp_motion(sample(1, 1), slow=2.0).total_seconds == pytest.approx(2 * m.total_seconds) + + +def test_play_empty_gripper_goes_home(): + fake = FakeArm(gripper_reading=1.0) # po zacisku odczyt ~pusto + ctl, _ = waypoint_arm(fake) + assert ctl.play(grasp_motion(sample(100, 200))) is False + assert fake.pose["shoulder_lift"] == pytest.approx(arm_mod.HOME_POSE["shoulder_lift"]) + + +def test_play_holding_returns_true(): + fake = FakeArm(gripper_reading=20.0) + ctl, _ = waypoint_arm(fake) + assert ctl.play(grasp_motion(sample(100, 200, lift=10.0))) is True + assert fake.pose["shoulder_lift"] == pytest.approx(10.0) # konczy nad szyszka, HOME robi wolajacy diff --git a/tools/act_pick.py b/tools/act_pick.py new file mode 100644 index 0000000..5bf330d --- /dev/null +++ b/tools/act_pick.py @@ -0,0 +1,115 @@ +"""Chwyt szyszki polityka ACT, potem wrzut do sloika nagranym ruchem (dwa etapy). + +Etap 1 (uczony): `lerobot-rollout` z wytrenowanym ACT przez --grasp-s sekund. ACT byl uczony +tylko na chwycie: z HOME do szyszki, zamkniecie chwytaka, lekkie uniesienie +(dataset local/so101_grasp, docs/POLICIES_LEROBOT.md). +Etap 2 (deterministyczny): `tools/arm_play.py --motion drop_box --home-after` - nad sloik, +otwarcie, powrot tym samym torem, HOME. Sloik stoi na robocie, wiec tego nie trzeba uczyc. + +Uzycie (na Pi, z katalogu repo; nic innego nie moze trzymac portu ramienia): + python tools/act_pick.py --policy ~/models/act_grasp/pretrained_model --dry-run # tylko komendy + python tools/act_pick.py --policy ~/models/act_grasp/pretrained_model + python tools/act_pick.py --policy ... --repeat 3 # 3 cykle chwyt + wrzut + python tools/act_pick.py --policy ... --skip-drop # sam chwyt (test polityki) + +Podglad kamery w czasie chwytu: http://:8081/ (tools/cam_preview.py, --preview-port 0 wylacza). +W trakcie wrzutu (etap 2) kamera jest zamknieta, strona wznawia obraz przy nastepnym chwycie. + +Rollout konczy sie z --robot.disable_torque_on_disconnect=false: ramie trzyma szyszke +w powietrzu, zanim arm_play przejmie port. Pierwsze uruchomienie z wylacznikiem w rece. +""" +from __future__ import annotations + +import argparse +import os +import shlex +import subprocess +import sys + +REPO_ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + +TASK = "Grasp the pine cone and lift it" # musi byc ten sam co --dataset.single_task przy nagraniu +CAMERA_SERIAL = "030522070668" # D435 na ramieniu (lerobot-find-cameras realsense) + + +def rollout_cmd(policy: str, seconds: float, port: str = "/dev/robot-arm", robot_id: str = "so101", + camera_serial: str = CAMERA_SERIAL, fps: int = 30, device: str = "cpu", + exe: str = ".venv/bin/lerobot-rollout") -> list: + """Komenda etapu 1. Kamera musi miec ten sam klucz i rozdzielczosc co w datasecie (wrist 640x480). + + device jawnie: wagi z laptopa maja w configu "cuda", a na Pi jej nie ma. + """ + cameras = (f"{{ wrist: {{type: intelrealsense, serial_number_or_name: {camera_serial}, " + f"width: 640, height: 480, fps: {fps}}}}}") + return [ + exe, + "--strategy.type=base", + f"--policy.path={policy}", + "--robot.type=so101_follower", + f"--robot.port={port}", + f"--robot.id={robot_id}", + f"--robot.cameras={cameras}", + "--robot.disable_torque_on_disconnect=false", + f"--task={TASK}", + f"--duration={seconds:g}", + f"--fps={fps}", + f"--device={device}", + "--play_sounds=false", + ] + + +def with_preview(cmd: list, preview_port: int, python: str = ".venv/bin/python") -> list: + """Ta sama komenda lerobot, ale przez tools/cam_preview.py: podglad kamery w tym samym procesie. + + preview_port <= 0 = bez podgladu (komenda bez zmian). + """ + if preview_port <= 0: + return cmd + entry = os.path.basename(cmd[0]) + return [python, "tools/cam_preview.py", f"--preview-port={preview_port}", entry] + cmd[1:] + + +def drop_cmd(port: str = "/dev/robot-arm", motion: str = "drop_box", python: str = ".venv/bin/python") -> list: + """Komenda etapu 2: nagrany ruch do sloika i HOME na koniec.""" + return [python, "tools/arm_play.py", "--motion", motion, "--port", port, "--home-after"] + + +def plan(args) -> list: + """Lista komend dla wszystkich cykli (etap 1, etap 2, etap 1, ...).""" + steps = [] + for _ in range(args.repeat): + steps.append(with_preview(rollout_cmd(args.policy, args.grasp_s, port=args.port, fps=args.fps), + args.preview_port)) + if not args.skip_drop: + steps.append(drop_cmd(port=args.port, motion=args.motion)) + return steps + + +def main(argv=None, run=subprocess.run) -> int: + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--policy", required=True, help="katalog pretrained_model z lerobot-train") + parser.add_argument("--grasp-s", type=float, default=15.0, help="czas etapu 1 [s] (jak episode_time_s)") + parser.add_argument("--port", default=os.environ.get("ROBOT_ARM_PORT", "/dev/robot-arm")) + parser.add_argument("--fps", type=int, default=30) + parser.add_argument("--motion", default="drop_box", help="ruch etapu 2 (motions/.json)") + parser.add_argument("--repeat", type=int, default=1) + parser.add_argument("--skip-drop", action="store_true", help="tylko etap 1") + parser.add_argument("--preview-port", type=int, default=8081, + help="podglad kamery http://:PORT/ w czasie chwytu (0 = wylacz)") + parser.add_argument("--dry-run", action="store_true", help="wypisz komendy, nic nie uruchamiaj") + args = parser.parse_args(argv) + + steps = plan(args) + for cmd in steps: + print("$", " ".join(shlex.quote(c) for c in cmd), flush=True) + if args.dry_run: + continue + rc = run(cmd, cwd=REPO_ROOT).returncode + if rc != 0: + print(f"Przerwane: kod wyjscia {rc}", file=sys.stderr) + return rc + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tools/cam_preview.py b/tools/cam_preview.py new file mode 100644 index 0000000..0b375a1 --- /dev/null +++ b/tools/cam_preview.py @@ -0,0 +1,205 @@ +"""Podglad kamery w przegladarce W TYM SAMYM procesie co lerobot (record / rollout / teleoperate). + +RealSense (i kazda kamera USB) moze byc otwarta tylko przez jeden proces. Osobny +`rs_mjpeg_server.py` gryzie sie wiec z `lerobot-record` i `lerobot-rollout` +(`Couldn't resolve requests` / `No device connected`). Ten skrypt uruchamia komende +lerobot w swoim procesie i obok niej serwer MJPEG, ktory PODGLADA klatki tej samej +kamery (`read_latest()` - nie zabiera klatek petli sterowania, nie otwiera kamery drugi raz). + +Uzycie na Pi (opcje podgladu PRZED nazwa komendy, dalej argumenty lerobot bez zmian): + .venv/bin/python tools/cam_preview.py lerobot-record --robot.type=so101_follower ... + .venv/bin/python tools/cam_preview.py --preview-port 8081 --preview-fps 10 lerobot-rollout ... + +Potem w przegladarce: http://:8081/ (8080 = rs_mjpeg_server, 8000 = jazda, 8010 = ramie). +Ctrl+C trafia do lerobot jak zwykle; serwer podgladu konczy sie razem z procesem. +""" +from __future__ import annotations + +import argparse +import socket +import sys +import threading +import time +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer + +CAMERAS: list = [] # kamery lerobot utworzone w tym procesie (wypelnia install_hook) +_cameras_lock = threading.Lock() + + +def register(cam) -> None: + with _cameras_lock: + CAMERAS.append(cam) + + +def install_hook() -> None: + """Kazda kamera lerobot (RealSense, OpenCV, ...) zapisuje sie do CAMERAS przy tworzeniu. + + Import leniwy: lerobot jest tylko na Pi / maszynie z ramieniem. + """ + from lerobot.cameras.camera import Camera + + if getattr(Camera.__init__, "_cam_preview", False): + return + orig_init = Camera.__init__ + + def init(self, config, *args, **kwargs): + orig_init(self, config, *args, **kwargs) + register(self) + + init._cam_preview = True + Camera.__init__ = init + + +def grab_jpeg(cam, quality: int = 70): + """Ostatnia klatka kamery jako JPEG albo None (kamera jeszcze nie gotowa / rozlaczona).""" + import cv2 + + try: + frame = cam.read_latest(max_age_ms=2000) + except Exception: + return None + if frame is None or getattr(frame, "ndim", 0) != 3: + return None + mode = getattr(cam, "color_mode", None) + if getattr(mode, "value", mode) == "rgb": # lerobot domyslnie oddaje RGB, JPEG z cv2 chce BGR + frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) + ok, buf = cv2.imencode(".jpg", frame, [cv2.IMWRITE_JPEG_QUALITY, quality]) + return buf.tobytes() if ok else None + + +def _index_html() -> bytes: + with _cameras_lock: + names = [str(c) for c in CAMERAS] + if not names: + body = "

Brak kamer (lerobot jeszcze sie laczy). Odswiezam...

" + head = '' + else: + head = "" + body = "".join( + f'
{n}
' + for i, n in enumerate(names)) + return (f"Podglad kamer{head}" + "" + "" + f"{body}").encode() + + +def make_handler(fps: float, quality: int): + period = 1.0 / max(fps, 0.5) + + class Handler(BaseHTTPRequestHandler): + def log_message(self, *args): # nie zasmiecaj logu lerobot + pass + + def _camera(self): + name = self.path.split("?")[0].rsplit("/", 1)[-1] + try: + idx = int(name.split(".")[0]) + with _cameras_lock: + return CAMERAS[idx], name.split(".", 1)[1] + except (ValueError, IndexError): + return None, None + + def do_GET(self): + if self.path.split("?")[0] in ("/", "/index.html"): + self._send(200, "text/html; charset=utf-8", _index_html()) + return + cam, ext = self._camera() if self.path.startswith("/cam/") else (None, None) + if cam is None: + self._send(404, "text/plain", b"nie ma takiej kamery") + elif ext == "jpg": + jpg = grab_jpeg(cam, quality) + if jpg is None: + self._send(503, "text/plain", b"brak klatki") + else: + self._send(200, "image/jpeg", jpg) + elif ext == "mjpg": + self._stream(cam) + else: + self._send(404, "text/plain", b"?") + + def _send(self, code, ctype, data): + self.send_response(code) + self.send_header("Content-Type", ctype) + self.send_header("Content-Length", str(len(data))) + self.send_header("Cache-Control", "no-store") + self.end_headers() + self.wfile.write(data) + + def _stream(self, cam): + self.send_response(200) + self.send_header("Content-Type", "multipart/x-mixed-replace; boundary=frame") + self.send_header("Cache-Control", "no-store") + self.end_headers() + misses = 0 + try: + while True: + jpg = grab_jpeg(cam, quality) + if jpg is None: + misses += 1 + if misses > 50 and not getattr(cam, "is_connected", True): + return # kamera zamknieta (koniec rollout) - przegladarka sprobuje ponownie + time.sleep(0.2) + continue + misses = 0 + self.wfile.write(b"--frame\r\nContent-Type: image/jpeg\r\n" + + f"Content-Length: {len(jpg)}\r\n\r\n".encode() + jpg + b"\r\n") + time.sleep(period) + except (BrokenPipeError, ConnectionResetError, OSError): + return + + return Handler + + +def start_server(host: str = "0.0.0.0", port: int = 8081, fps: float = 10, quality: int = 70): + """Serwer MJPEG w watku-demonie. Zwraca serwer (server.server_address[1] = port).""" + server = ThreadingHTTPServer((host, port), make_handler(fps, quality)) + server.daemon_threads = True + threading.Thread(target=server.serve_forever, name="cam_preview", daemon=True).start() + return server + + +def resolve_entry(name: str): + """Funkcja main() komendy lerobot (np. lerobot-rollout) z entry points pakietu.""" + from importlib.metadata import entry_points + + eps = entry_points(group="console_scripts", name=name) + if not eps: + raise SystemExit(f"Nie znam komendy {name!r} (np. lerobot-record, lerobot-rollout, lerobot-teleoperate)") + return next(iter(eps)).load() + + +def main(argv=None, resolve=resolve_entry, hook=install_hook) -> int: + parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + parser.add_argument("--preview-port", type=int, default=8081) + parser.add_argument("--preview-host", default="0.0.0.0") + parser.add_argument("--preview-fps", type=float, default=10, help="klatek/s w podgladzie (CPU Pi)") + parser.add_argument("--preview-quality", type=int, default=70, help="jakosc JPEG 1-100") + parser.add_argument("entry", help="komenda lerobot, np. lerobot-rollout") + parser.add_argument("args", nargs=argparse.REMAINDER, help="argumenty komendy bez zmian") + args = parser.parse_args(argv) + + entry = resolve(args.entry) + hook() + try: + server = start_server(args.preview_host, args.preview_port, args.preview_fps, args.preview_quality) + print(f"[cam_preview] podglad: http://{socket.gethostname()}.local:{server.server_address[1]}/", flush=True) + except OSError as e: # port zajety: podglad jest dodatkiem, robot jedzie dalej + server = None + print(f"[cam_preview] BEZ podgladu, port {args.preview_port}: {e} (fuser -k {args.preview_port}/tcp)", + file=sys.stderr, flush=True) + + sys.argv = [args.entry] + args.args # lerobot (draccus) czyta argumenty z sys.argv + try: + rc = entry() + finally: + if server is not None: + server.shutdown() + server.server_close() + return rc if isinstance(rc, int) else 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tools/drive_calib.py b/tools/drive_calib.py new file mode 100644 index 0000000..fd55f8d --- /dev/null +++ b/tools/drive_calib.py @@ -0,0 +1,261 @@ +""" +Kalibracja jazdy bez miarki: droga z glebi RealSense, kat z zyroskopu telefonu (phyphox). + + python tools/drive_calib.py # 2x prosto + 2x obrot, raport, nic nie zapisuje + python tools/drive_calib.py --write # to samo + wpis do pinecone_config.json + +Przygotowanie: +- Kamera patrzy do przodu na sciane / pudlo 1-3 m przed robotem (srodek kadru), ramie nieruchome. + Przed robotem wolne miejsce na oba przejazdy (domyslnie 0.2 + 0.4 m). +- Telefon plasko na bazie, ekranem do gory, phyphox "Gyroscope (rotation rate)" z "Allow remote access". +- Wylacznik w rece. web_control.py wylaczony (trzyma port Xiao). + +Co mierzy (fazy czasowe, open-loop, jak tools/base_test.py; komendy SUROWE, bez control.steer_sign): +- prosto z --speeds: droga = glebia przed - glebia po, znos kursu z zyroskopu; +- obroty z --turn-rates (na zmiane w lewo i w prawo, robot wraca do kierunku): kat z zyroskopu, + po pierwszym pytanie do operatora, w ktora strone sie obrocil (zyroskop sam nie odrozni + zlego steer_sign od zlego heading.sign). +Z dwoch punktow PWM -> predkosc dopasowuje prosta pwm = p0 + s*v, stad xiao_pwm_min/max +(i xiao_steer_min/max dla obrotow) przy obecnym control.v_max / w_max. +""" +from __future__ import annotations + +import argparse +import math +import os +import sys +import time + +ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +if ROOT not in sys.path: + sys.path.insert(0, ROOT) + +from pinecone_bot.base import XIAO_SPEED_LIMIT, XIAO_STEER_LIMIT, xiao_map # noqa: E402 +from pinecone_bot.config import Config # noqa: E402 + +SETTLE_S = 1.0 # po zatrzymaniu: robot sie toczy, zyroskop dobiera probki +MIN_MOVE_M = 0.02 # ponizej tego uznajemy, ze kola nie ruszyly (tarcie statyczne) +MIN_TURN_DEG = 3.0 +MAX_WALL_M = 3.5 # dalej glebia D4xx jest za szumna na pomiar cm +MIN_WALL_M = 0.4 # blizej glebia D4xx ma dziury (MinZ ~0.3 m przy 640x480) +ROI_FRAC = 0.2 # srodkowy kwadrat kadru (20% szerokosci i wysokosci) +MIN_VALID_FRAC = 0.3 + + +def wall_distance(depth_frames, roi_frac: float = ROI_FRAC) -> float | None: + """Mediana glebi [m] ze srodka kadru po wielu klatkach; None, gdy za malo pikseli z glebia.""" + import numpy as np + + meds = [] + for d in depth_frames: + if d is None: + continue + h, w = d.shape[:2] + rh, rw = max(1, int(h * roi_frac / 2)), max(1, int(w * roi_frac / 2)) + roi = d[h // 2 - rh:h // 2 + rh, w // 2 - rw:w // 2 + rw] + valid = roi[roi > 0.15] + if valid.size >= MIN_VALID_FRAC * roi.size: + meds.append(float(np.median(valid))) + if len(meds) < max(1, len(depth_frames) // 2): + return None + return float(np.median(meds)) + + +def fit_pwm(points: list[tuple[int, float]], full_scale: float) -> tuple[int, int] | None: + """(pwm, zmierzona predkosc) -> (pwm_min, pwm_max) dla xiao_map przy full_scale. + + Prosta pwm = p0 + s*v najmniejszymi kwadratami. None, gdy za malo punktow albo wynik bez sensu. + """ + pts = [(abs(p), abs(v)) for p, v in points if abs(v) > 1e-6] + if len(pts) < 2 or len({v for _, v in pts}) < 2: + return None + n = len(pts) + mv = sum(v for _, v in pts) / n + mp = sum(p for p, _ in pts) / n + s = sum((v - mv) * (p - mp) for p, v in pts) / sum((v - mv) ** 2 for _, v in pts) + p0 = mp - s * mv + if s <= 0 or p0 < 0: + return None + return int(round(p0)), int(round(p0 + s * full_scale)) + + +def signs_from_turn(w_cmd: float, dyaw: float, turned_left: bool, heading_sign: float) -> tuple[float, float]: + """(control.steer_sign, heading.sign) z jednego obrotu. + + w_cmd > 0 = SUROWA komenda w lewo (bez steer_sign). dyaw = zmiana kursu z zyroskopu + przy obecnym heading.sign. Obrot w lewo ma dawac dodatni kurs. + """ + steer_sign = 1.0 if (w_cmd > 0) == turned_left else -1.0 + gyro_ok = (dyaw > 0) == turned_left + return steer_sign, heading_sign if gyro_ok else -heading_sign + + +class Calibrator: + def __init__(self, cfg: Config, base, gyro, read_depth, ask, sleep=time.sleep, log=print): + self.cfg, self.base, self.gyro = cfg, base, gyro + self.read_depth, self.ask, self.sleep, self.log = read_depth, ask, sleep, log + + def _yaw(self) -> float: + for _ in range(30): + y = self.gyro.yaw() + if y is not None: + return y + self.sleep(0.1) + raise RuntimeError("brak danych z phyphox (telefon, Allow remote access, ekran wlaczony?)") + + def _wall(self, n: int = 10) -> float: + d = wall_distance([self.read_depth() for _ in range(n)]) + if d is None or d > MAX_WALL_M: + raise RuntimeError("brak sciany przed kamera (srodek kadru bez glebi albo dalej niz %.1f m)" % MAX_WALL_M) + return d + + def _drive(self, v: float, w: float, seconds: float) -> None: + self.base.set_speed(v, w) + try: + self.sleep(seconds) + finally: + self.base.stop() + self.sleep(SETTLE_S) + + def forward(self, v: float, seconds: float) -> dict: + c, b = self.cfg.control, self.cfg.base + pwm = xiao_map(v, c.v_max, b.xiao_pwm_min, b.xiao_pwm_max, XIAO_SPEED_LIMIT) + d0, y0 = self._wall(), self._yaw() + if d0 - 1.5 * v * seconds < MIN_WALL_M: + raise RuntimeError("za blisko sciany (%.2f m) na przejazd %.2f m - odsun robota" % (d0, v * seconds)) + self._drive(v, 0.0, seconds) + d1, y1 = self._wall(), self._yaw() + dist = d0 - d1 + r = {"v_cmd": v, "pwm": pwm, "seconds": seconds, "wall_before_m": round(d0, 3), + "wall_after_m": round(d1, 3), "dist_m": round(dist, 3), "v_real": round(dist / seconds, 4), + "drift_deg": round(math.degrees(y1 - y0), 1)} + self.log("[prosto v=%.2f pwm=%d] %.3f m -> %.3f m: przejechal %.3f m (%.3f m/s), znos kursu %+.1f st" + % (v, pwm, d0, d1, dist, r["v_real"], r["drift_deg"])) + if dist < -MIN_MOVE_M: + self.log(" UWAGA: sciana sie ODDALILA - robot cofal (odwrocony kierunek jazdy w Xiao?) albo kamera patrzy do tylu") + elif abs(dist) < MIN_MOVE_M: + self.log(" kola nie ruszyly: pwm %d ponizej tarcia statycznego" % pwm) + return r + + def turn(self, w: float, seconds: float) -> dict: + c, b = self.cfg.control, self.cfg.base + pwm = abs(xiao_map(w, c.w_max, b.xiao_steer_min, b.xiao_steer_max, XIAO_STEER_LIMIT)) + y0 = self._yaw() + self._drive(0.0, w, seconds) + dyaw = self._yaw() - y0 + r = {"w_cmd": w, "pwm": pwm, "seconds": seconds, "dyaw_deg": round(math.degrees(dyaw), 1), + "w_real": round(abs(dyaw) / seconds, 4)} + self.log("[obrot w=%+.2f pwm=%d] kurs %+.1f st (%.3f rad/s)" % (w, pwm, r["dyaw_deg"], r["w_real"])) + return r + + def run(self, speeds, seconds, turn_rates, turn_seconds) -> dict: + fwd = [self.forward(v, seconds) for v in speeds] + turns, steer_sign, heading_sign = [], None, None + for i, w in enumerate(turn_rates): + w = abs(w) if i % 2 == 0 else -abs(w) # lewo, prawo, lewo...: wraca do kierunku startu + r = self.turn(w, turn_seconds) + turns.append(r) + if steer_sign is None and abs(r["dyaw_deg"]) >= MIN_TURN_DEG: + left = self.ask("W ktora strone obrocil sie robot (patrzac z gory)? [l/p]: ") + steer_sign, heading_sign = signs_from_turn(w, math.radians(r["dyaw_deg"]), left, + self.cfg.heading.sign) + c = self.cfg.control + moved = [r for r in fwd if r["dist_m"] > MIN_MOVE_M] + rotated = [r for r in turns if abs(r["dyaw_deg"]) >= MIN_TURN_DEG] + return { + "forward": fwd, "turns": turns, + "steer_sign": steer_sign, "heading_sign": heading_sign, + "xiao_pwm": fit_pwm([(r["pwm"], r["v_real"]) for r in moved], c.v_max), + "xiao_steer": fit_pwm([(r["pwm"], r["w_real"]) for r in rotated], c.w_max), + } + + +def apply(cfg: Config, res: dict) -> list[str]: + """Wpisuje wyniki do cfg. Zwraca liste zmian (tylko pola, ktore udalo sie zmierzyc).""" + changes = [] + + def put(obj, name, value, label): + old = getattr(obj, name) + if value is not None and old != value: + setattr(obj, name, value) + changes.append("%s: %s -> %s" % (label, old, value)) + + put(cfg.control, "steer_sign", res["steer_sign"], "control.steer_sign") + put(cfg.heading, "sign", res["heading_sign"], "heading.sign") + if res["xiao_pwm"]: + put(cfg.base, "xiao_pwm_min", res["xiao_pwm"][0], "base.xiao_pwm_min") + put(cfg.base, "xiao_pwm_max", res["xiao_pwm"][1], "base.xiao_pwm_max") + if res["xiao_steer"]: + put(cfg.base, "xiao_steer_min", res["xiao_steer"][0], "base.xiao_steer_min") + put(cfg.base, "xiao_steer_max", res["xiao_steer"][1], "base.xiao_steer_max") + return changes + + +def ask_left(prompt: str) -> bool: + while True: + a = input(prompt).strip().lower() + if a in ("l", "p"): + return a == "l" + + +def main(argv=None) -> int: + p = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) + p.add_argument("--config", default=None) + p.add_argument("--port", default=None, help="port Xiao (domyslnie cfg.base.port)") + p.add_argument("--speeds", type=float, nargs="+", default=[0.10, 0.20], help="m/s, >= 2 do dopasowania PWM") + p.add_argument("--seconds", type=float, default=2.0) + p.add_argument("--turn-rates", type=float, nargs="+", default=[0.3, 0.5], help="rad/s, >= 2") + p.add_argument("--turn-seconds", type=float, default=2.0) + p.add_argument("--write", action="store_true", help="zapisz wyniki do pliku configu") + p.add_argument("--yes", action="store_true", help="bez potwierdzenia startu") + args = p.parse_args(argv) + + cfg = Config.load(args.config) + cfg.base.driver = "xiao" + if args.port: + cfg.base.port = args.port + need = sum(args.speeds) * args.seconds + print("Robot pojedzie prosto ok. %.2f m (+ zapas), potem obroty w miejscu. Wylacznik w rece." % need) + if not args.yes and input("Start? [t/N]: ").strip().lower() != "t": + return 1 + + from pinecone_bot.base import make_base + from pinecone_bot.camera import RealSenseCamera + from pinecone_bot.heading import PhyphoxGyro + + camera = gyro = base = None + try: + camera = RealSenseCamera(cfg, depth=True) + gyro = PhyphoxGyro(cfg.heading).start() + base = make_base(cfg) + cal = Calibrator(cfg, base, gyro, read_depth=lambda: camera.read()[1], ask=ask_left) + res = cal.run(args.speeds, args.seconds, args.turn_rates, args.turn_seconds) + except (RuntimeError, KeyboardInterrupt) as e: + print("Przerwane: %s" % (e or "Ctrl+C")) + return 2 + finally: + if base is not None: + base.stop() + base.close() + if gyro is not None: + gyro.close() + if camera is not None: + camera.close() + + changes = apply(cfg, res) + print("\nWynik:") + for line in changes or ["bez zmian wzgledem configu"]: + print(" " + line) + if res["xiao_pwm"] is None: + print(" PWM jazdy nie dopasowane (potrzeba 2 przejazdow, w ktorych robot ruszyl)") + if res["xiao_steer"] is None: + print(" PWM obrotu nie dopasowane (potrzeba 2 obrotow > %.0f st)" % MIN_TURN_DEG) + if args.write and changes: + print("Zapisane: %s" % cfg.save(args.config)) + elif changes: + print("Nic nie zapisane (dodaj --write).") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tools/live_grasp/.gitignore b/tools/live_grasp/.gitignore new file mode 100644 index 0000000..89f9ac0 --- /dev/null +++ b/tools/live_grasp/.gitignore @@ -0,0 +1 @@ +out/ diff --git a/tools/live_grasp/README.md b/tools/live_grasp/README.md new file mode 100644 index 0000000..f9a2058 --- /dev/null +++ b/tools/live_grasp/README.md @@ -0,0 +1,14 @@ +# live_grasp - chwytanie szyszki samym ramieniem (eksperyment 2026-09-26) + +Instrukcja krok po kroku i stan prac: `docs/LIVE_GRASP.md`. + +Skrypty ida na Pi przez ssh stdin (`pi.sh`), nic nie jest kopiowane na Pi. `percept.py` (glebia: plaszczyzna +ziemi, wysokosc, szyszki) jest doklejany przed kazdym skryptem. + +- `teach_clean.py` - uczenie: HOME -> 1 szyszka -> chwyt reka -> zapis (glowna droga dalej) +- `fit.py` - model szyszka (kamera w HOME) -> stawy chwytu, blad "zostaw-jedna" +- `pick.py`, `pick_loop.py` - autonomiczny chwyt z modelu + sloik +- `rec.py`, `replay.py` - nagranie ruchu reka i odtworzenie cyklu +- `snap_only.py`, `step.py` - zdjecie / pojedynczy ruch +- `attempt.py`, `servo.py`, `grasp_servo.py` - proby, ktore nie zadzialaly (opis w docs) +- `data/` - nagranie 2 min (stawy), sesje uczenia 1-2 (bez zdjec), model, sciezka do sloika diff --git a/tools/live_grasp/attempt.py b/tools/live_grasp/attempt.py new file mode 100644 index 0000000..035f38a --- /dev/null +++ b/tools/live_grasp/attempt.py @@ -0,0 +1,251 @@ +# Jedna proba zebrania szyszki, samo ramie. Wysylane na Pi przez stdin (python -), bez plikow na Pi. +# Wejscie: STATE (json, podstawiany przez run.sh). Wyjscie na stderr: log, "RESULT ...", "STATE {...}". +# Na stdout: base64 jpg ze zdjecia po podniesieniu. +import base64, json, sys, time +import cv2 +import arm_control as ac +from pinecone_bot.config import Config +from pinecone_bot.arm import WaypointArm +from pinecone_bot.camera import RealSenseCamera +from pinecone_bot.detector import HsvConeDetector + +S = json.loads(STATE) +HOME = {"shoulder_pan": -5.45, "shoulder_lift": 88.92, "elbow_flex": 7.56, "wrist_flex": -87.87, "wrist_roll": 88.88} +PRE = {"shoulder_lift": 86.64, "elbow_flex": -3.87, "wrist_flex": -87.87, "wrist_roll": 88.97} +GR = {"shoulder_lift": 89.1, "elbow_flex": -13.36, "wrist_flex": -65.54, "wrist_roll": 88.09} +SEARCH_PANS = [-15, -5, -25, 5, -35, 15] +T0 = time.time() + + +def log(*a): + print(f"[{time.time() - T0:5.1f}s]", *a, file=sys.stderr, flush=True) + + +cfg = Config.load() +arm = ac.make_arm(port="/dev/robot-arm", max_relative_target=None) +for i in range(3): + try: + arm.bus.connect() + break + except Exception as e: # magistrala czasem nie widzi serwa chwytaka przy scisku + log("connect nieudany, ponawiam:", str(e).splitlines()[0]) + try: + arm.bus.port_handler.closePort() + except Exception: + pass + time.sleep(1.0) +else: + raise SystemExit("RESULT BLAD magistrala serw") +cur = arm.bus.sync_read("Present_Position") +arm.bus.sync_write("Goal_Position", cur) +arm.bus.enable_torque() +ctl = WaypointArm(cfg, arm=arm) +cam = RealSenseCamera(cfg, depth=True, laser_power=360) +hsv_det = HsvConeDetector(cfg.detector) +intr = cam.intrinsics + + +class Det: + def __init__(self, px, py, area, bbox): + self.px, self.py, self.area, self.bbox, self.partial = px, py, area, bbox, False + + +HVIEW = {"H": None} + + +def depth_cones(depth): + """Szyszki = wybrzuszenia 1.2-8 cm nad plaszczyzna ziemi (dziala po ciemku: glebia to podczerwien z laserem).""" + valid = (depth > 0.1) & (depth < 1.5) + P = xyz_image(depth, intr) + g = fit_ground(P, valid) + if g is None: + return [] + n, d = g + H = np.where(valid, P @ n + d, 0.0) + HVIEW["H"] = np.where(valid, H, -1.0) + low = ((H > 0.012) & (H < 0.08) & valid).astype(np.uint8) + low = cv2.morphologyEx(low, cv2.MORPH_OPEN, np.ones((5, 5), np.uint8)) + num, lab, st, cent = cv2.connectedComponentsWithStats(low, connectivity=8) + out = [] + for i in range(1, num): + a = st[i, cv2.CC_STAT_AREA] + if 150 <= a <= 9000: + out.append(Det(float(cent[i][0]), float(cent[i][1]), float(a), tuple(int(x) for x in st[i, :4]))) + return out + + +def pose(): + return arm.bus.sync_read("Present_Position") + + +def go(target, sec, fix=True): + ctl._move(target, sec) + time.sleep(0.35) + if fix: # regulacja calkujaca: niskie P serw + grawitacja zostawiaja blad, dopisz go do celu (max 4 razy) + cmd = dict(target) + for _ in range(4): + now = pose() + err = {j: target[j] - now[j] for j in target if j != "gripper"} + if all(abs(e) <= 0.8 for e in err.values()): + break + cmd = {j: (cmd[j] + max(-6.0, min(6.0, err[j])) if j in err and abs(err[j]) > 0.8 else cmd[j]) for j in cmd} + ctl._move(cmd, 0.4) + time.sleep(0.3) + return pose() + + +def in_jaws(d): + x0, y0, x1, y1 = S["jaw_box"] + return x0 <= d.px <= x1 and d.py >= y0 or d.py >= S["body_y"] + + +def snap(): + for _ in range(4): + bgr, depth = cam.read() + dets = [d for d in depth_cones(depth) if not in_jaws(d)] + # kolor (przy swietle): widzi szyszke tez w cieniu stereo szczeki, gdzie glebia jest slepa + for c in hsv_det.detect(bgr): + if c.partial or in_jaws(c) or c.area < 150: + continue + if all(abs(c.px - d.px) > 30 or abs(c.py - d.py) > 30 for d in dets): + dets.append(Det(c.px, c.py, c.area, c.bbox)) + Hv = HVIEW["H"] if HVIEW["H"] is not None else np.zeros(depth.shape) + vis = cv2.applyColorMap(cv2.convertScaleAbs(np.clip(Hv, 0, 0.06), alpha=255.0 / 0.06), cv2.COLORMAP_JET) + vis[Hv < 0] = 0 # brak pomiaru (cien stereo, za blisko) + for d in dets: + x, y, w, h = d.bbox + cv2.rectangle(vis, (x, y), (x + w, y + h), (255, 255, 255), 2) + cv2.drawMarker(vis, (int(S["align_x"]), int(S["align_y"])), (255, 0, 255), cv2.MARKER_CROSS, 30, 2) + last_img["img"] = np.hstack((cv2.resize(bgr, (480, 360)), cv2.resize(vis, (480, 360)))) + return bgr, dets + + +def nearest(dets, x, y): + return min(dets, key=lambda d: (d.px - x) ** 2 + (d.py - y) ** 2) if dets else None + + +def reachable(d): + return abs(d.py - S["align_y"]) <= S["reach_py"] + + +result = "BRAK" +img = None +last_img = {"img": None} +try: + pan = pose()["shoulder_pan"] + go({**PRE, "shoulder_pan": pan, "gripper": 100.0}, 1.5) + # 1. szukanie + target = None + for p in [None] + SEARCH_PANS: + if p is not None: + pan = go({**PRE, "shoulder_pan": float(p), "gripper": 100.0}, 1.0)["shoulder_pan"] + bgr, dets = snap() + # podstawa ma w serwie zakres tylko +-23 st: bierz szyszki, do ktorych dosiegnie + ok = [d for d in dets if reachable(d)] + log(f"pan {pan:.1f}: szyszki {[(round(d.px), round(d.py)) for d in dets]}, w zasiegu {len(ok)}") + if ok: + target = nearest(ok, S["align_x"], S["align_y"]) + break + if target is None: + result = "BRAK szyszki w zasiegu" + raise StopIteration + # 2. celowanie: podstawa (pan + -> szyszka w lewo, k px/st) i bark (lift - -> szyszka w dol, ky px/st) + tx, ty = target.px, target.py + bw, bh = target.bbox[2], target.bbox[3] + lift = pose()["shoulder_lift"] + for it in range(10): + ex, ey = tx - S["align_x"], ty - S["align_y"] + log(f"celowanie {it}: szyszka ({tx:.0f},{ty:.0f}) blad x {ex:+.0f} y {ey:+.0f}, k={S['k']:.1f} ky={S['ky']:.1f}") + if abs(ex) <= S["tol_px"] and abs(ey) <= S["tol_py"]: + break + dpan = max(-12.0, min(12.0, ex / S["k"])) if abs(ex) > S["tol_px"] else 0.0 + dlift = max(-6.0, min(6.0, ey / S["ky"])) if abs(ey) > S["tol_py"] else 0.0 + new_lift = max(S["lift_min"], min(S["lift_max"], lift + dlift)) + before = (pan, lift) + now = go({**PRE, "shoulder_pan": pan + dpan, "shoulder_lift": new_lift, "gripper": 100.0}, 0.8) + pan, lift = now["shoulder_pan"], now["shoulder_lift"] + mp, ml = pan - before[0], lift - before[1] + bgr, dets = snap() + px_pred, py_pred = tx - S["k"] * mp, ty - S["ky"] * ml + seen = nearest(dets, px_pred, py_pred) + if seen is None or abs(seen.px - px_pred) > 80 or abs(seen.py - py_pred) > 80: + # zgubiona (pod szczeka): nie zgaduj, cofnij pol kroku i zmierz jeszcze raz + log(f" szyszka zgubiona (przewidziane {px_pred:.0f},{py_pred:.0f}) - cofam pol kroku") + now = go({**PRE, "shoulder_pan": pan - mp / 2, "shoulder_lift": lift - ml / 2, "gripper": 100.0}, 0.8) + pan, lift = now["shoulder_pan"], now["shoulder_lift"] + bgr, dets = snap() + seen = nearest(dets, tx - S["k"] * (pan - before[0]), ty - S["ky"] * (lift - before[1])) + if seen is None: + raise_lost = True + log(" nadal nie widac szyszki - przerywam bez chwytu") + result = "ZGUBIONA" + raise StopIteration + tx, ty = seen.px, seen.py + bw, bh = seen.bbox[2], seen.bbox[3] + continue + # uczenie wspolczynnikow z obserwacji (tylko os, ktora sie wyraznie ruszyla) + if abs(mp) >= 1.0 and abs(ml) < 0.5: + k_obs = -(seen.px - tx) / mp + if 3.0 < k_obs < 40.0: + S["k"] = round(0.6 * S["k"] + 0.4 * k_obs, 2) + if abs(ml) >= 1.0 and abs(mp) < 0.5: + ky_obs = -(seen.py - ty) / ml + if 1.5 < ky_obs < 30.0: + S["ky"] = round(0.6 * S["ky"] + 0.4 * ky_obs, 2) + tx, ty = seen.px, seen.py + bw, bh = seen.bbox[2], seen.bbox[3] + else: + result = "NIE WYCELOWANO" + log(" 10 krokow bez trafienia w punkt celowania - bez chwytu") + raise StopIteration + dl = lift - PRE["shoulder_lift"] + aligned = (round(tx), round(ty)) + # 3. chwyt + # szyszka lezy w poprzek (szersza niz wyzsza na obrazie) -> obroc szczeki o 90 st, zeby zlapac ja przez srednice + roll_off = S["roll_turn"] if bw > S["wide_ratio"] * bh else 0.0 + grasp = {**GR, "shoulder_pan": pan} + grasp["wrist_roll"] += roll_off + grasp["shoulder_lift"] = max(S["lift_min"], min(S["lift_max"], GR["shoulder_lift"] + dl)) + grasp["elbow_flex"] += S["goff_elbow"] + grasp["wrist_flex"] += S["goff_wrist"] + if roll_off: + log(f"szyszka w poprzek (bbox {bw}x{bh}) -> obracam chwytak o {roll_off:+.0f} st") + go({**PRE, "shoulder_pan": pan, "shoulder_lift": lift, "wrist_roll": PRE["wrist_roll"] + roll_off, "gripper": 100.0}, 1.2) + log(f"schodze: pan {pan:.1f} lift {grasp['shoulder_lift']:.1f}, glebokosc elbow {S['goff_elbow']:+.1f} wrist {S['goff_wrist']:+.1f}") + go({**grasp, "gripper": 100.0}, 1.5) + ctl._move({"gripper": 0.0}, 1.0) + time.sleep(0.4) + g = pose()["gripper"] + held = g >= cfg.arm.empty_gripper_below + log(f"chwytak {g:.1f} -> {'TRZYMA' if held else 'PUSTY'}") + hold = max(g - 3.0, 0.0) if held else 0.0 + ctl._move({"gripper": hold}, 0.3) # mniejszy scisk: przy celu 0 serwo chwytaka potrafi zniknac z magistrali + go({**PRE, "shoulder_pan": pan, "shoulder_lift": lift, "wrist_roll": PRE["wrist_roll"] + roll_off, "gripper": hold}, 1.5) + img, dets = snap() + left = nearest(dets, *aligned) + shift = None if left is None or abs(left.px - aligned[0]) > 120 or abs(left.py - aligned[1]) > 120 else (round(left.px - aligned[0]), round(left.py - aligned[1])) + S["tries"] += 1 + if held: + S["ok"] += 1 + result = "MAM SZYSZKE" + else: + result = "PUSTO" + log(f"po probie szyszka przesunieta o {shift} px (None = nie widac)") + S["log"] = (S["log"] + [{"pan": round(pan, 1), "lift": round(lift, 1), "aligned": aligned, "roll": roll_off, "bbox": [bw, bh], "gripper": round(g, 1), "shift": shift if not held else None, "result": result}])[-20:] +except StopIteration: + pass +finally: + try: + g = pose()["gripper"] + go({**HOME, "gripper": g}, 2.0) + if result == "MAM SZYSZKE": + log("HOME z szyszka - zabierz ja z chwytaka (4 s), potem otwieram") + time.sleep(4.0) + ctl._move({"gripper": 100.0}, 0.8) + finally: + arm.bus.disconnect(disable_torque=False) + cam.close() +log("RESULT", result) +print("STATE " + json.dumps(S), file=sys.stderr) +if last_img["img"] is not None: + print(base64.b64encode(cv2.imencode(".jpg", last_img["img"])[1].tobytes()).decode()) diff --git a/tools/live_grasp/data/grasp_model.json b/tools/live_grasp/data/grasp_model.json new file mode 100644 index 0000000..2e3c1bf --- /dev/null +++ b/tools/live_grasp/data/grasp_model.json @@ -0,0 +1,59 @@ +{ + "quad": false, + "W": [ + [ + -17.947631888566082, + 158.75300214660868, + -29.897168770159475, + -248.86122426350852 + ], + [ + 107.08316368413739, + 9.497874497320026, + 63.1014954062063, + -60.27628916458241 + ], + [ + 22.377985324920516, + -293.43893312434216, + 105.91810732291862, + 703.6738769086737 + ] + ], + "joints": [ + "shoulder_pan", + "shoulder_lift", + "elbow_flex", + "wrist_flex" + ], + "approach_delta": [ + 0.51, + 0.93, + -0.75, + -0.66 + ], + "wrist_roll": 87.58375000000001, + "n": 8, + "p_min": [ + -0.045, + -0.0607, + 0.2461 + ], + "p_max": [ + 0.1209, + 0.0831, + 0.2885 + ], + "q_min": [ + -20.13, + 60.97, + -15.3, + -85.76 + ], + "q_max": [ + 5.01, + 88.4, + 23.12, + -14.81 + ] +} \ No newline at end of file diff --git a/tools/live_grasp/data/grasp_servo_state.json b/tools/live_grasp/data/grasp_servo_state.json new file mode 100644 index 0000000..c7bd75b --- /dev/null +++ b/tools/live_grasp/data/grasp_servo_state.json @@ -0,0 +1 @@ +{"tries": 5, "max_corr": 5, "J2": [[-7.56, -1.49], [-2.7, -9.35]], "tip_off": [-45.0, 25.0], "tol_u": 15, "tol_v": 20, "dq_bias": [0.16, -0.28, 0.07, -0.31], "wrist_extra": 0.0, "ok": 0, "up_elbow": 20.0, "up_wrist": 10.0} diff --git a/tools/live_grasp/data/jar_cycle.json b/tools/live_grasp/data/jar_cycle.json new file mode 100644 index 0000000..ee9c027 --- /dev/null +++ b/tools/live_grasp/data/jar_cycle.json @@ -0,0 +1 @@ +[{"t": 58.506, "shoulder_pan": -20.04, "shoulder_lift": 82.77, "elbow_flex": 4.13, "wrist_flex": -97.89, "wrist_roll": 81.93, "gripper": 18.2}, {"t": 58.628, "shoulder_pan": -16.53, "shoulder_lift": 91.12, "elbow_flex": -7.65, "wrist_flex": -97.63, "wrist_roll": 82.02, "gripper": 18.2}, {"t": 58.762, "shoulder_pan": -11.6, "shoulder_lift": 91.56, "elbow_flex": -11.78, "wrist_flex": -89.63, 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"elbow_flex": -67.69, "wrist_flex": -68.0, "wrist_roll": 88.79, "gripper": 3.43}, {"t": 219.04, "shoulder_pan": 20.92, "shoulder_lift": -12.97, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.79, "gripper": 3.43}, {"t": 219.07, "shoulder_pan": 20.92, "shoulder_lift": -14.9, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.79, "gripper": 3.43}, {"t": 219.11, "shoulder_pan": 20.92, "shoulder_lift": -16.48, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.53, "gripper": 3.43}, {"t": 219.14, "shoulder_pan": 20.92, "shoulder_lift": -17.71, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.53, "gripper": 3.43}, {"t": 219.18, "shoulder_pan": 20.92, "shoulder_lift": -19.47, "elbow_flex": -67.78, "wrist_flex": -68.09, "wrist_roll": 88.53, "gripper": 3.43}, {"t": 219.21, "shoulder_pan": 20.92, "shoulder_lift": -21.76, "elbow_flex": -67.78, "wrist_flex": -68.09, "wrist_roll": 88.53, "gripper": 3.43}, {"t": 219.25, "shoulder_pan": 20.92, "shoulder_lift": -24.31, "elbow_flex": -67.78, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.28, "shoulder_pan": 20.84, "shoulder_lift": -26.59, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.32, "shoulder_pan": 20.84, "shoulder_lift": -28.53, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.35, "shoulder_pan": 20.84, "shoulder_lift": -30.11, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.39, "shoulder_pan": 20.75, "shoulder_lift": -31.69, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.42, "shoulder_pan": 20.66, "shoulder_lift": -33.45, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.46, "shoulder_pan": 19.96, "shoulder_lift": -35.03, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.49, "shoulder_pan": 18.46, "shoulder_lift": -36.62, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.53, "shoulder_pan": 16.53, "shoulder_lift": -38.2, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.56, "shoulder_pan": 14.15, "shoulder_lift": -39.6, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.6, "shoulder_pan": 11.69, "shoulder_lift": -41.01, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.63, "shoulder_pan": 9.41, "shoulder_lift": -42.42, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.67, "shoulder_pan": 7.21, "shoulder_lift": -43.74, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.7, "shoulder_pan": 5.36, "shoulder_lift": -45.14, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.74, "shoulder_pan": 3.6, "shoulder_lift": -46.46, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.77, "shoulder_pan": 2.2, "shoulder_lift": -47.69, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.81, "shoulder_pan": 0.7, "shoulder_lift": -48.92, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.84, "shoulder_pan": -0.7, "shoulder_lift": -50.15, "elbow_flex": -67.69, "wrist_flex": -68.09, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 219.88, "shoulder_pan": -2.2, "shoulder_lift": -51.47, "elbow_flex": -67.69, "wrist_flex": -68.18, "wrist_roll": 88.62, "gripper": 3.43}, {"t": 219.91, "shoulder_pan": -3.52, "shoulder_lift": -52.79, "elbow_flex": -67.69, "wrist_flex": -68.18, "wrist_roll": 88.7, "gripper": 3.43}, {"t": 219.95, "shoulder_pan": -4.75, "shoulder_lift": -54.29, "elbow_flex": -67.69, "wrist_flex": -68.26, "wrist_roll": 88.79, "gripper": 3.43}, {"t": 219.98, "shoulder_pan": -5.8, "shoulder_lift": -55.78, "elbow_flex": -67.69, "wrist_flex": -68.26, "wrist_roll": 88.79, "gripper": 3.43}, {"t": 220.02, "shoulder_pan": -6.77, "shoulder_lift": -57.45, "elbow_flex": -67.69, "wrist_flex": -68.26, "wrist_roll": 88.88, "gripper": 3.43}, {"t": 220.05, "shoulder_pan": -7.74, "shoulder_lift": -59.12, "elbow_flex": -67.69, "wrist_flex": -68.26, "wrist_roll": 88.88, "gripper": 3.43}, {"t": 220.09, "shoulder_pan": -8.7, "shoulder_lift": -60.7, "elbow_flex": -67.69, "wrist_flex": -68.26, "wrist_roll": 88.88, "gripper": 3.43}, {"t": 220.12, "shoulder_pan": -9.58, "shoulder_lift": -62.29, "elbow_flex": -67.69, "wrist_flex": -68.26, "wrist_roll": 88.88, "gripper": 3.43}, {"t": 220.16, "shoulder_pan": -10.46, "shoulder_lift": -63.69, "elbow_flex": -67.69, "wrist_flex": -68.26, "wrist_roll": 88.88, "gripper": 3.43}, {"t": 220.19, "shoulder_pan": -11.16, "shoulder_lift": -64.75, "elbow_flex": -67.69, "wrist_flex": -68.26, "wrist_roll": 88.88, "gripper": 3.43}, {"t": 220.23, "shoulder_pan": -11.78, "shoulder_lift": -65.54, "elbow_flex": -67.69, "wrist_flex": -68.26, "wrist_roll": 88.88, "gripper": 3.43}, {"t": 220.26, "shoulder_pan": -12.31, "shoulder_lift": -65.98, "elbow_flex": -67.69, "wrist_flex": -68.35, "wrist_roll": 88.7, "gripper": 3.43}, {"t": 220.3, "shoulder_pan": -12.92, "shoulder_lift": -66.07, "elbow_flex": -67.69, "wrist_flex": -69.05, "wrist_roll": 88.62, "gripper": 3.43}, {"t": 220.33, "shoulder_pan": -13.63, "shoulder_lift": -66.07, "elbow_flex": -67.69, "wrist_flex": -70.64, "wrist_roll": 88.53, "gripper": 3.43}, {"t": 220.37, "shoulder_pan": -14.33, "shoulder_lift": -65.98, "elbow_flex": -67.69, "wrist_flex": -73.19, "wrist_roll": 88.53, "gripper": 3.43}, {"t": 220.4, "shoulder_pan": -14.95, "shoulder_lift": -65.71, "elbow_flex": -67.69, "wrist_flex": -76.35, "wrist_roll": 88.53, "gripper": 3.43}, {"t": 220.44, "shoulder_pan": -15.47, "shoulder_lift": -65.54, "elbow_flex": -67.69, "wrist_flex": -79.87, "wrist_roll": 88.53, "gripper": 3.43}, {"t": 220.47, "shoulder_pan": -15.82, "shoulder_lift": -65.45, "elbow_flex": -67.69, "wrist_flex": -83.74, "wrist_roll": 88.53, "gripper": 3.43}, {"t": 220.51, "shoulder_pan": -16.0, "shoulder_lift": -65.36, "elbow_flex": -67.25, "wrist_flex": -87.43, "wrist_roll": 88.53, "gripper": 3.43}, {"t": 220.54, "shoulder_pan": -16.0, "shoulder_lift": -65.36, "elbow_flex": -67.08, "wrist_flex": -90.77, "wrist_roll": 88.62, "gripper": 3.43}, {"t": 220.58, "shoulder_pan": -16.0, "shoulder_lift": -65.63, "elbow_flex": -66.99, "wrist_flex": -93.41, "wrist_roll": 88.7, "gripper": 3.43}, {"t": 220.61, "shoulder_pan": -16.09, "shoulder_lift": -65.98, "elbow_flex": -66.99, "wrist_flex": -95.6, "wrist_roll": 88.79, "gripper": 3.43}, {"t": 220.65, "shoulder_pan": -16.09, "shoulder_lift": -65.98, "elbow_flex": -66.99, "wrist_flex": -97.27, "wrist_roll": 88.79, "gripper": 3.43}, {"t": 220.68, "shoulder_pan": -16.18, "shoulder_lift": -65.98, "elbow_flex": -66.99, "wrist_flex": -98.33, "wrist_roll": 88.88, "gripper": 3.43}, {"t": 220.72, "shoulder_pan": -16.62, "shoulder_lift": -65.98, "elbow_flex": -66.99, "wrist_flex": -99.03, "wrist_roll": 88.88, "gripper": 3.43}, {"t": 220.75, "shoulder_pan": -17.67, "shoulder_lift": -65.98, "elbow_flex": -67.08, "wrist_flex": -99.38, "wrist_roll": 88.88, "gripper": 3.43}, {"t": 220.79, "shoulder_pan": -18.99, "shoulder_lift": -66.07, "elbow_flex": -67.52, "wrist_flex": -99.38, "wrist_roll": 88.88, "gripper": 3.43}, {"t": 220.82, "shoulder_pan": -20.04, "shoulder_lift": -66.15, "elbow_flex": -67.52, "wrist_flex": -99.38, "wrist_roll": 88.79, "gripper": 3.43}, {"t": 220.86, "shoulder_pan": -20.48, "shoulder_lift": -66.33, "elbow_flex": -67.6, "wrist_flex": -99.21, "wrist_roll": 88.53, "gripper": 3.43}, {"t": 220.89, "shoulder_pan": -20.84, "shoulder_lift": -66.42, "elbow_flex": -67.6, "wrist_flex": -98.95, "wrist_roll": 88.44, "gripper": 3.43}, {"t": 220.93, "shoulder_pan": -21.01, "shoulder_lift": -66.95, "elbow_flex": -67.6, "wrist_flex": -98.95, "wrist_roll": 88.35, "gripper": 3.43}, {"t": 220.96, "shoulder_pan": -21.01, "shoulder_lift": -68.26, "elbow_flex": -67.6, "wrist_flex": -98.95, "wrist_roll": 88.35, "gripper": 3.43}, {"t": 221.0, "shoulder_pan": -21.01, "shoulder_lift": -69.76, "elbow_flex": -67.6, "wrist_flex": -99.21, "wrist_roll": 88.35, "gripper": 3.89}, {"t": 221.03, "shoulder_pan": -20.75, "shoulder_lift": -71.16, "elbow_flex": -67.25, "wrist_flex": -99.21, "wrist_roll": 88.35, "gripper": 4.02}, {"t": 221.07, "shoulder_pan": -20.48, "shoulder_lift": -72.31, "elbow_flex": -67.08, "wrist_flex": -98.95, "wrist_roll": 88.35, "gripper": 4.02}, {"t": 221.1, "shoulder_pan": -20.48, "shoulder_lift": -73.27, "elbow_flex": -66.99, "wrist_flex": -98.86, "wrist_roll": 88.35, "gripper": 4.08}, {"t": 221.14, "shoulder_pan": -20.48, "shoulder_lift": -73.8, "elbow_flex": -66.9, "wrist_flex": -98.86, "wrist_roll": 88.35, "gripper": 4.08}, {"t": 221.17, "shoulder_pan": -20.57, "shoulder_lift": -73.63, "elbow_flex": -66.9, "wrist_flex": -99.21, "wrist_roll": 88.35, "gripper": 4.08}, {"t": 221.21, "shoulder_pan": -20.84, "shoulder_lift": -73.54, "elbow_flex": -66.99, "wrist_flex": -99.3, "wrist_roll": 88.26, "gripper": 4.15}, {"t": 221.24, "shoulder_pan": -20.84, "shoulder_lift": -73.45, "elbow_flex": -67.43, "wrist_flex": -99.38, "wrist_roll": 88.26, "gripper": 4.21}, {"t": 221.28, "shoulder_pan": -20.66, "shoulder_lift": -73.71, "elbow_flex": -67.52, "wrist_flex": -99.38, "wrist_roll": 88.26, "gripper": 4.27}, {"t": 221.31, "shoulder_pan": -20.48, "shoulder_lift": -74.15, "elbow_flex": -67.25, "wrist_flex": -99.38, "wrist_roll": 88.26, "gripper": 4.73}, {"t": 221.35, "shoulder_pan": -20.4, "shoulder_lift": -74.24, "elbow_flex": -67.25, "wrist_flex": -99.12, "wrist_roll": 88.26, "gripper": 5.76}, {"t": 221.38, "shoulder_pan": -20.4, "shoulder_lift": -74.24, "elbow_flex": -67.25, "wrist_flex": -99.21, "wrist_roll": 88.26, "gripper": 7.32}, {"t": 221.42, "shoulder_pan": -20.4, "shoulder_lift": -74.15, "elbow_flex": -67.34, "wrist_flex": -99.3, "wrist_roll": 88.26, "gripper": 9.2}, {"t": 221.45, "shoulder_pan": -20.66, "shoulder_lift": -74.07, "elbow_flex": -67.52, "wrist_flex": -99.38, "wrist_roll": 88.26, "gripper": 11.53}]} +{"kind": "home", "t": 229.78, "cones": [{"uv": [363.3, 183.1], "p": [0.0189, -0.0291, 0.2826], "h": 0.0178, "area": 1188}, {"uv": [168.1, 423.9], "p": [-0.0642, 0.074, 0.2553], "h": 0.0169, "area": 1344}], "ground": [-0.0227, -0.2741, -0.9614, 0.279]} diff --git a/tools/live_grasp/fit.py b/tools/live_grasp/fit.py new file mode 100644 index 0000000..c07a0f5 --- /dev/null +++ b/tools/live_grasp/fit.py @@ -0,0 +1,79 @@ +# Dopasowanie modelu z uczenia: szyszka (3D w kamerze w HOME) -> stawy chwytu. +# Uzycie (laptop): python fit.py data/teach_sessionN.jsonl data/grasp_model.json +import json, sys, numpy as np +J4 = ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex"] +recs = [json.loads(l) for l in open(sys.argv[1] if len(sys.argv) > 1 else "teach.jsonl")] +samples, ambiguous = [], [] +for i, r in enumerate(recs): + if r["kind"] != "grasp": + continue + before = next((recs[k] for k in range(i - 1, -1, -1) if recs[k]["kind"] == "home"), None) + after = next((recs[k] for k in range(i + 1, len(recs)) if recs[k]["kind"] == "home"), None) + if before is None or after is None: + continue + gone = [] + for c in before["cones"]: + dmin = min((np.linalg.norm(np.subtract(c["p"], a["p"])) for a in after["cones"]), default=9) + if dmin > 0.03: + gone.append(c) + tr = r["traj"] + tg = next((p["t"] for p in tr if abs(p["gripper"] - r["q"]["gripper"]) < 1.0 and p["t"] > tr[0]["t"] + 0.8), tr[-1]["t"]) + pre = min(tr, key=lambda p: abs(p["t"] - (tg - 0.7))) + item = {"q": r["q"], "pre": pre, "gone": gone} + if len(gone) == 1: + samples.append({"p": gone[0]["p"], "uv": gone[0]["uv"], "q": r["q"], "pre": pre}) + elif gone: + ambiguous.append(item) +# niejednoznaczne: szyszka zgodna z katem podstawy (u ~ a + b * pan z czystych probek) +if len(samples) >= 3 and ambiguous: + A = np.array([[1, s_["q"]["shoulder_pan"]] for s_ in samples]); u = np.array([s_["uv"][0] for s_ in samples]) + ab, *_ = np.linalg.lstsq(A, u, rcond=None) + print(f"u = {ab[0]:.0f} + {ab[1]:.1f} * pan") + for it in ambiguous: + pred = ab[0] + ab[1] * it["q"]["shoulder_pan"] + best = min(it["gone"], key=lambda c: abs(c["uv"][0] - pred)) + others = sorted(abs(c["uv"][0] - pred) for c in it["gone"]) + if abs(best["uv"][0] - pred) < 60 and (len(others) < 2 or others[1] - others[0] > 60): + samples.append({"p": best["p"], "uv": best["uv"], "q": it["q"], "pre": it["pre"], "resolved": True}) + print(f" rozstrzygniete: pan {it['q']['shoulder_pan']:.1f} -> szyszka u {best['uv'][0]:.0f} (przewidziane {pred:.0f})") + else: + print(f" nadal niejednoznaczne: pan {it['q']['shoulder_pan']:.1f}, kandydaci u {[c['uv'][0] for c in it['gone']]}, przewidziane {pred:.0f}") +print(len(samples), "probek") +if not samples: + sys.exit() +X = np.array([s["p"] for s in samples]) +Y = np.array([[s["q"][j] for j in J4] for s in samples]) +for s in samples: + print("szyszka p", np.round(s["p"], 3), "uv", s["uv"], "-> q", [s["q"][j] for j in J4]) + + +def feats(P, quad): + P = np.atleast_2d(P) + x, z = P[:, 0], P[:, 2] + f = [np.ones(len(P)), x, z] + if quad: + f += [x * x, z * z, x * z] + return np.stack(f, 1) + + +for quad in (False, True): + if len(samples) < (8 if quad else 4): + continue + errs = [] + for k in range(len(samples)): + m = np.arange(len(samples)) != k + W, *_ = np.linalg.lstsq(feats(X[m], quad), Y[m], rcond=None) + errs.append(feats(X[k], quad) @ W - Y[k]) + errs = np.abs(np.array(errs)).reshape(-1, 4) + print(("kwadratowy" if quad else "liniowy"), "blad zostaw-jedna [st] sredni", np.round(errs.mean(0), 1), "max", np.round(errs.max(0), 1)) +quad = len(samples) >= 10 +W, *_ = np.linalg.lstsq(feats(X, quad), Y, rcond=None) +dpre = np.mean([[s["pre"][j] - s["q"][j] for j in J4] for s in samples], 0) +model = {"quad": quad, "W": W.tolist(), "joints": J4, "approach_delta": dpre.round(2).tolist(), + "wrist_roll": float(np.mean([s["q"]["wrist_roll"] for s in samples])), "n": len(samples), + "p_min": X.min(0).tolist(), "p_max": X.max(0).tolist(), + "q_min": Y.min(0).tolist(), "q_max": Y.max(0).tolist()} +out = sys.argv[2] if len(sys.argv) > 2 else "grasp_model.json" +json.dump(model, open(out, "w"), indent=1) +print("zapisano", out) +print("model:", "kwadratowy" if quad else "liniowy", "podejscie delta", np.round(dpre, 1)) diff --git a/tools/live_grasp/grasp_servo.py b/tools/live_grasp/grasp_servo.py new file mode 100644 index 0000000..e2195b4 --- /dev/null +++ b/tools/live_grasp/grasp_servo.py @@ -0,0 +1,244 @@ +# Proby chwytu z uczeniem po kazdej probie (doklejane za percept.py; MODEL, STATE podstawia runner). +# 1) HOME, glebia -> szyszka -> poza chwytu z modelu + nauczona poprawka dq_bias. +# 2) w pozie chwytu (szczeki otwarte) zdjecie: gdzie szyszka wzgledem srodka miedzy koncowkami szczek. +# 3) korekta podstawa (bok) i bark (odleglosc), 1 s ruchu + zdjecie, jakobian 2x2 uczony (Broyden). +# 4) zacisk; pusto -> uczenie: glebokosc nadgarstka / punkt docelowy miedzy szczekami. Stan wraca jako STATE. +import base64, json, sys, time +import arm_control as ac +from pinecone_bot.config import Config +from pinecone_bot.arm import WaypointArm +from pinecone_bot.camera import RealSenseCamera + +M = json.loads(MODEL) +S = json.loads(STATE) +J4 = M["joints"] +HOME = {"shoulder_pan": -5.45, "shoulder_lift": 88.3, "elbow_flex": 7.56, "wrist_flex": -87.87, "wrist_roll": 88.88} +LIM = {"shoulder_pan": (-22.5, 22.5), "shoulder_lift": (55.0, 88.3), "elbow_flex": (-40.0, 35.0), "wrist_flex": (-100.0, -10.0)} +JAR_UP = [(None, 74.4, -63.3, -72.8, 1.2), (None, 36.7, -77.5, -72.9, 1.2), (None, -35.3, -78.8, -73.7, 1.6), + (-18.1, -38.5, -98.8, -74.0, 1.2), (-19.0, -57.8, -97.8, -84.7, 1.0), (-22.2, -60.8, -97.7, -84.6, 1.0)] +JAR_BACK = [(-21.8, -59.7, -76.0, -80.3, 1.0), (-21.9, -45.1, -33.3, -80.3, 1.0), (-21.8, -29.8, -3.1, -80.3, 1.0), + (-13.2, 60.1, 17.8, -80.2, 2.0)] +T0 = time.time() + + +def log(*a): + print(f"[{time.time() - T0:5.1f}s]", *a, file=sys.stderr, flush=True) + + +cfg = Config.load() +arm = ac.make_arm(port="/dev/robot-arm", max_relative_target=None) +arm.bus.connect() +cur = arm.bus.sync_read("Present_Position") +arm.bus.sync_write("Goal_Position", cur) +arm.bus.enable_torque() +ctl = WaypointArm(cfg, arm=arm) +cam = RealSenseCamera(cfg, depth=True, laser_power=360) +intr = cam.intrinsics + + +def pose(): + return arm.bus.sync_read("Present_Position") + + +def clampq(q): + return {j: (max(LIM[j][0], min(LIM[j][1], v)) if j in LIM else v) for j, v in q.items()} + + +def go(target, sec): + target = clampq(target) + ctl._move(target, sec) + time.sleep(0.25) + cmd = dict(target) + for _ in range(3): + now = pose() + err = {j: target[j] - now[j] for j in target if j != "gripper"} + if all(abs(e) <= 0.8 for e in err.values()): + break + cmd = {j: (cmd[j] + max(-6.0, min(6.0, err[j])) if j in err and abs(err[j]) > 0.8 else cmd[j]) for j in cmd} + ctl._move(cmd, 0.4) + time.sleep(0.2) + return pose() + + +def frames(): + for _ in range(5): + bgr, depth = cam.read() + return bgr, depth + + +def blue_mask(bgr): + hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV) + m = cv2.inRange(hsv, (88, 100, 90), (120, 255, 255)) + return cv2.morphologyEx(m, cv2.MORPH_OPEN, np.ones((5, 5), np.uint8)) > 0 + + +def cones_in(bgr, depth, exclude): + valid = (depth > 0.1) & (depth < 1.5) + P = xyz_image(depth, intr) + g = fit_ground(P, valid) + if g is None: + return [], None + H = np.where(valid, P @ g[0] + g[1], 0.0) + ex = cv2.dilate(exclude.astype(np.uint8), np.ones((5, 5), np.uint8)) > 0 + low = ((H > 0.012) & (H < 0.08) & valid & ~ex & ~((H > 0.08) & valid)).astype(np.uint8) + low = cv2.morphologyEx(low, cv2.MORPH_OPEN, np.ones((5, 5), np.uint8)) + num, lab, st, cent = cv2.connectedComponentsWithStats(low, connectivity=8) + out = [] + for i in range(1, num): + a = int(st[i, cv2.CC_STAT_AREA]) + if 200 <= a <= 12000 and not (cent[i][1] > 380 and 330 < cent[i][0] < 580): + out.append({"uv": np.array([cent[i][0], cent[i][1]], float), "p": P[lab == i].mean(0), "area": a}) + return out, H + + +def jaw_target(bgr): + """Cel = koncowka widocznej (ruchomej) szczeki + nauczone przesuniecie. W pozie chwytu kamera widzi jedna szczeke.""" + bm = blue_mask(bgr) + num, lab, st, _ = cv2.connectedComponentsWithStats(bm.astype(np.uint8), connectivity=8) + comps = [i for i in range(1, num) if st[i, cv2.CC_STAT_AREA] > 800] + if not comps: + return None, bm, [] + i = min(comps, key=lambda i: st[i, cv2.CC_STAT_TOP]) # najwyzej siegajacy obszar = szczeka + ys, xs = np.nonzero(lab == i) + sel = ys <= ys.min() + 15 + tip = np.array([xs[sel].mean(), ys[sel].mean()]) + return tip + np.array(S["tip_off"]), bm, [tip] + + +def cone_near(cones, uv, max_px=150): + c = min(cones, key=lambda c: np.linalg.norm(c["uv"] - uv), default=None) + return c if c is not None and np.linalg.norm(c["uv"] - uv) <= max_px else None + + +def feats(p): + x, z = p[0], p[2] + return np.array([1.0, x, z] + ([x * x, z * z, x * z] if M["quad"] else [])) + + +sheet, result = [], "NIE ZLAPANA" + + +def shot(bgr, tgt, tips, cone, label): + im = bgr.copy() + for t in tips: + cv2.circle(im, (int(t[0]), int(t[1])), 6, (255, 0, 255), -1) + if tgt is not None: + cv2.drawMarker(im, (int(tgt[0]), int(tgt[1])), (255, 255, 0), cv2.MARKER_CROSS, 30, 2) + if cone is not None: + cv2.circle(im, (int(cone["uv"][0]), int(cone["uv"][1])), 16, (255, 255, 255), 2) + cv2.putText(im, label, (8, 30), 0, 0.9, (0, 255, 255), 2) + sheet.append(cv2.resize(im, (320, 240))) + + +try: + for k in range(S["tries"]): + # 1. HOME i szyszka + go({**HOME, "gripper": 30.0}, 2.5) + bgr, depth = frames() + cones, _ = cones_in(bgr, depth, blue_mask(bgr)) + if not cones: + result = "BRAK SZYSZKI W KADRZE" + break + c0 = min(cones, key=lambda c: c["p"][2]) + q = feats(c0["p"]) @ np.array(M["W"]) + np.array(S["dq_bias"]) + grasp = clampq({j: float(q[i]) for i, j in enumerate(J4)}) + grasp["wrist_flex"] += S["wrist_extra"] + grasp["wrist_roll"] = M["wrist_roll"] + # nad szyszka: szczeki wyzej (lokiec do tylu, nadgarstek w gore), podstawa jak do chwytu + high = dict(grasp) + high["elbow_flex"] = grasp["elbow_flex"] - S["up_elbow"] + high["wrist_flex"] = grasp["wrist_flex"] - S["up_wrist"] + log(f"proba {k + 1}: szyszka HOME uv {np.round(c0['uv'])} -> chwyt {({j: round(v, 1) for j, v in grasp.items()})}") + h0 = pose() + go({**{j: h0[j] for j in J4}, "elbow_flex": high["elbow_flex"], "wrist_flex": high["wrist_flex"], + "wrist_roll": M["wrist_roll"], "gripper": 100.0}, 1.5) # 1. w gore w miejscu + go({**high, "gripper": 100.0}, 1.5) # 2. obrot i bark w powietrzu + now = pose() + # 2-3. korekta w pozie chwytu: podstawa + bark, jakobian 2x2 (px na stopien) + Jm = np.array(S["J2"]) + prev_uv, aligned = None, False + for it in range(S["max_corr"]): + bgr, depth = frames() + tgt, bm, tips = jaw_target(bgr) + cones, _ = cones_in(bgr, depth, bm) + cone = cone_near(cones, tgt if prev_uv is None else prev_uv) if tgt is not None else None + shot(bgr, tgt, tips, cone, f"{k + 1}.{it}") + if tgt is None or cone is None: + log(f" korekta {it}: {'brak szczek' if tgt is None else 'szyszki nie widac (zaslonieta?)'}") + aligned = cone is None and prev_uv is not None and np.linalg.norm(prev_uv - tgt) < 60 if tgt is not None else False + break + e = cone["uv"] - tgt + log(f" korekta {it}: szyszka {np.round(cone['uv'])} cel {np.round(tgt)} blad {np.round(e)} px") + if abs(e[0]) <= S["tol_u"] and abs(e[1]) <= S["tol_v"]: + aligned = True + break + # e = J @ dq -> dq = -J^-1 e (przy uwarunkowaniu: tlumione) + dq = -np.linalg.solve(Jm.T @ Jm + 4.0 * np.eye(2), Jm.T @ e) + dq = np.clip(dq, -5.0, 5.0) + before = pose() + now = go({**{j: before[j] for j in J4}, "wrist_roll": M["wrist_roll"], "gripper": 100.0, + "shoulder_pan": before["shoulder_pan"] + dq[0], "shoulder_lift": before["shoulder_lift"] + dq[1]}, 1.0) + dq_act = np.array([now["shoulder_pan"] - before["shoulder_pan"], now["shoulder_lift"] - before["shoulder_lift"]]) + if prev_uv is not None or True: + bgr2, depth2 = frames() + tgt2, bm2, _ = jaw_target(bgr2) + cones2, _ = cones_in(bgr2, depth2, bm2) + c2 = cone_near(cones2, cone["uv"] - Jm @ dq_act) if tgt2 is not None else None + if c2 is not None and np.abs(dq_act).max() >= 1.0: + de = (c2["uv"] - tgt2) - e + Jm = Jm + 0.5 * np.outer(de - Jm @ dq_act, dq_act) / (dq_act @ dq_act) # uczenie px/stopien + prev_uv = None if c2 is None else c2["uv"] + S["J2"] = np.round(Jm, 2).tolist() + # 3. pionowo w dol: tylko lokiec i nadgarstek wracaja do wartosci chwytu + here = pose() + go({**{j: here[j] for j in J4}, "elbow_flex": here["elbow_flex"] + S["up_elbow"], + "wrist_flex": here["wrist_flex"] + S["up_wrist"], "wrist_roll": M["wrist_roll"], "gripper": 100.0}, 1.5) + # 4. zacisk + ctl._move({"gripper": 0.0}, 1.0) + time.sleep(0.4) + g = pose()["gripper"] + held = g >= cfg.arm.empty_gripper_below + qf = pose() + log(f" zacisk: chwytak {g:.1f} -> {'TRZYMA' if held else 'PUSTY'} (wycelowane: {aligned})") + # uczenie modelu: poprawka stawow = gdzie skonczyla korekta minus przewidywanie modelu + corr = np.array([qf[j] for j in J4]) - (feats(c0["p"]) @ np.array(M["W"])) + corr[3] -= S["wrist_extra"] + S["dq_bias"] = np.round(0.5 * np.array(S["dq_bias"]) + 0.5 * corr, 2).tolist() + if held: + hold = max(g - 4.0, 0.0) + ctl._move({"gripper": hold}, 0.3) + for pan, lift, elbow, wrist, sec in JAR_UP: + go({"shoulder_pan": qf["shoulder_pan"] if pan is None else pan, "shoulder_lift": lift, "elbow_flex": elbow, + "wrist_flex": wrist, "wrist_roll": M["wrist_roll"], "gripper": hold}, sec * 1.5) + ctl._move({"gripper": 100.0}, 0.8) + time.sleep(0.5) + for pan, lift, elbow, wrist, sec in JAR_BACK: + go({"shoulder_pan": pan, "shoulder_lift": lift, "elbow_flex": elbow, "wrist_flex": wrist, + "wrist_roll": M["wrist_roll"], "gripper": 60.0}, sec * 1.5) + result = f"ZLAPANA w probie {k + 1}, do sloika" + S["ok"] = S.get("ok", 0) + 1 + break + # pusto: wycelowana a pusto -> szczeki za wysoko: glebiej; niewycelowana -> tylko poprawka stawow + if aligned: + S["wrist_extra"] = round(min(S["wrist_extra"] + 3.0, 15.0), 1) + log(f" wycelowane, ale pusto -> glebiej: wrist_extra {S['wrist_extra']}") + ctl._move({"gripper": 100.0}, 0.6) + up = pose() + go({**{j: up[j] for j in J4}, "elbow_flex": up["elbow_flex"] - S["up_elbow"], "wrist_flex": up["wrist_flex"] - S["up_wrist"], + "wrist_roll": M["wrist_roll"], "gripper": 100.0}, 1.2) +except Exception as exc: + log("BLAD", repr(exc)) + raise +finally: + try: + go({**HOME, "gripper": 30.0}, 2.5) + finally: + arm.bus.disconnect(disable_torque=False) + cam.close() +log("RESULT", result) +print("STATE " + json.dumps(S), file=sys.stderr) +if sheet: + while len(sheet) % 3: + sheet.append(np.zeros_like(sheet[0])) + img = np.vstack([np.hstack(sheet[i:i + 3]) for i in range(0, len(sheet), 3)]) + print(base64.b64encode(cv2.imencode(".jpg", img)[1].tobytes()).decode()) diff --git a/tools/live_grasp/percept.py b/tools/live_grasp/percept.py new file mode 100644 index 0000000..2f43114 --- /dev/null +++ b/tools/live_grasp/percept.py @@ -0,0 +1,115 @@ +# Percepcja tylko z glebi (wklejana na poczatek programow wysylanych na Pi). +# Ziemia = plaszczyzna (RANSAC). Chwytak = to, co nad ziemia i dotyka dolnej krawedzi obrazu. +# Szyszki = osobne wybrzuszenia 1.2-9 cm nad ziemia. Wszystko w metrach, uklad kamery. +import numpy as np +import cv2 + +RNG = np.random.default_rng(0) + + +def xyz_image(depth, intr): + h, w = depth.shape + u = np.arange(w)[None, :] + v = np.arange(h)[:, None] + z = depth + return np.dstack(((u - intr.ppx) / intr.fx * z, (v - intr.ppy) / intr.fy * z, z)) + + +def fit_ground(P, valid, iters=150, tol=0.008): + h = P.shape[0] + sel = valid.copy() + sel[int(h * 0.55):, :] = False # dol obrazu to chwytak + pts = P[sel][::7] + if len(pts) < 200: + return None + best, best_n = None, 0 + for _ in range(iters): + a, b, c = pts[RNG.choice(len(pts), 3, replace=False)] + n = np.cross(b - a, c - a) + nn = np.linalg.norm(n) + if nn < 1e-9: + continue + n /= nn + d = -n @ a + cnt = int((np.abs(pts @ n + d) < tol).sum()) + if cnt > best_n: + best, best_n = (n, d), cnt + n, d = best + inl = pts[np.abs(pts @ n + d) < tol] # dopasowanie LSQ na inlierach + c = inl.mean(0) + n = np.linalg.svd(inl - c, full_matrices=False)[2][2] + d = -n @ c + if d < 0: # normalna w strone kamery: wysokosc > 0 nad ziemia + n, d = -n, -d + return n, d + + +def perceive(depth, intr, bgr): + """-> dict: ground (n,d), grip (tip 3D, uv, h), cones [ (p3D, uv, h, area) ], height map.""" + valid = (depth > 0.1) & (depth < 1.5) + P = xyz_image(depth, intr) + g = fit_ground(P, valid) + if g is None: + return None + n, d = g + H = np.where(valid, P @ n + d, 0.0) + # chwytak: jaskrawy niebieski PLA (duzo jasniejszy i bardziej nasycony niz trawa), najwiekszy obszar + h_img = depth.shape[0] + hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV) + blue = cv2.inRange(hsv, (88, 120, 140), (118, 255, 255)) + blue = cv2.morphologyEx(blue, cv2.MORPH_OPEN, np.ones((5, 5), np.uint8)) + num_g, lab_g, st_g, _ = cv2.connectedComponentsWithStats(blue, connectivity=8) + grip_ids = [i for i in range(1, num_g) if st_g[i, cv2.CC_STAT_AREA] > 1500] + gm = np.isin(lab_g, grip_ids) if grip_ids else np.zeros(depth.shape, bool) + gm_dil = cv2.dilate(gm.astype(np.uint8), np.ones((11, 11), np.uint8)) > 0 + # szyszki: 1.2-6 cm nad ziemia, poza chwytakiem + low = ((H > 0.012) & (H < 0.06) & valid & ~gm_dil).astype(np.uint8) + low = cv2.morphologyEx(low, cv2.MORPH_OPEN, np.ones((5, 5), np.uint8)) + num, lab, stats, cent = cv2.connectedComponentsWithStats(low, connectivity=8) + grip_lab = [] + out = {"ground": (n, d), "H": H, "grip": None, "cones": [], "lab": lab} + if gm.any(): + ys, xs = np.nonzero(gm) + vtop = ys.min() + tip = gm & (np.arange(h_img)[:, None] <= vtop + 20) + tipv = tip & valid + tp = P[tipv] if tipv.sum() >= 10 else P[gm & valid] + out["grip"] = {"p": tp.mean(0), "uv": (float(np.nonzero(tip)[1].mean()), float(vtop + 10)), + "h": float(H[tip].mean()), "mask": gm} + for i in range(1, num): + if i in grip_lab: + continue + a = stats[i, cv2.CC_STAT_AREA] + if a < 150 or a > 9000 or cent[i][1] > 390: # dol obrazu: kabel serwa, korpus chwytaka + continue + m = lab == i + hmax = float(H[m].max()) + out["cones"].append({"p": P[m].mean(0), "uv": (float(cent[i][0]), float(cent[i][1])), "h": hmax, "area": int(a), + "bbox": tuple(int(x) for x in stats[i, :4])}) + return out + + +def pick_cone(per, prefer_uv=None): + cs = per["cones"] + if not cs: + return None + if prefer_uv is not None: + return min(cs, key=lambda c: (c["uv"][0] - prefer_uv[0]) ** 2 + (c["uv"][1] - prefer_uv[1]) ** 2) + return max(cs, key=lambda c: c["area"]) + + +def debug_image(bgr, per, cone=None): + H = per["H"] if per else None + vis = cv2.applyColorMap(cv2.convertScaleAbs(np.clip(H, 0, 0.15), alpha=255 / 0.15), cv2.COLORMAP_JET) if H is not None \ + else np.zeros_like(bgr) + if per: + if per["grip"]: + u, v = per["grip"]["uv"] + for img in (bgr, vis): + cv2.drawMarker(img, (int(u), int(v)), (255, 0, 255), cv2.MARKER_CROSS, 26, 2) + for c in per["cones"]: + x, y, w, h = c["bbox"] + col = (0, 0, 255) if c is cone else (0, 200, 255) + for img in (bgr, vis): + cv2.rectangle(img, (x, y), (x + w, y + h), col, 2) + return np.hstack((cv2.resize(bgr, (480, 360)), cv2.resize(vis, (480, 360)))) diff --git a/tools/live_grasp/pi.sh b/tools/live_grasp/pi.sh new file mode 100644 index 0000000..b2253da --- /dev/null +++ b/tools/live_grasp/pi.sh @@ -0,0 +1,42 @@ +#!/bin/bash +# Uruchamia skrypt z tego katalogu na Pi przez ssh stdin (nic nie kopiuje na Pi). percept.py idzie zawsze pierwszy. +# Uzycie (git-bash na laptopie, z katalogu repo): +# bash tools/live_grasp/pi.sh snap_only.py # zdjecie + pozycje stawow -> out/snap_only.jpg +# bash tools/live_grasp/pi.sh pick.py MODEL=data/grasp_model.json # jedna proba z modelu +# bash tools/live_grasp/pi.sh teach_clean.py --bg teach # w tle na Pi: /tmp/teach.jsonl, /tmp/teach.err +# Zmienne VAR=plik wstawiaja do skryptu: VAR = r'''''' (MODEL, STATE, CYCLE, ARGS). +# --bg NAZWA: nohup na Pi, stdout -> /tmp/NAZWA.out, stderr -> /tmp/NAZWA.err (przezyje zerwanie wifi). +D="$(cd "$(dirname "$0")" && pwd)" +PI="${PI_HOST:-robot@robot.local}" +SCRIPT="$1"; shift +VARS=(); BG="" +while [ $# -gt 0 ]; do + case "$1" in + --bg) BG="$2"; shift 2 ;; + *=*) VARS+=("$1"); shift ;; + *) echo "nieznany argument: $1"; exit 1 ;; + esac +done +build() { + for v in "${VARS[@]}"; do + name="${v%%=*}"; file="${v#*=}" + [ -f "$file" ] || file="$D/$file" + echo "$name = r'''$(cat "$file")'''" + done + cat "$D/percept.py" "$D/$SCRIPT" +} +mkdir -p "$D/out" +base="$(basename "$SCRIPT" .py)" +if [ -n "$BG" ]; then + build | ssh -o BatchMode=yes "$PI" "cd ~/hackaton && nohup .venv/bin/python - > /tmp/$BG.out 2> /tmp/$BG.err" & + echo "w tle; log: ssh $PI tail -f /tmp/$BG.err" + exit 0 +fi +build | ssh -o BatchMode=yes "$PI" 'cd ~/hackaton && nohup .venv/bin/python -' > "$D/out/$base.b64" 2> "$D/out/$base.err" +grep -vE '^\s*$' "$D/out/$base.err" | grep -vE '^(STATE|POS|IMG) ' | tail -40 +python - "$D/out/$base.b64" "$D/out/$base.jpg" <<'PY' +import base64, sys +lines = [l for l in open(sys.argv[1]).read().splitlines() if l and not l.startswith(("POS ", "IMG "))] +if lines: + open(sys.argv[2], "wb").write(base64.b64decode(lines[-1])); print("zdjecie:", sys.argv[2]) +PY diff --git a/tools/live_grasp/pick.py b/tools/live_grasp/pick.py new file mode 100644 index 0000000..3e7800e --- /dev/null +++ b/tools/live_grasp/pick.py @@ -0,0 +1,139 @@ +# Zbieranie z modelu z uczenia (doklejane za percept.py; MODEL = json z fit.py). +# HOME -> zdjecie glebi -> szyszka -> stawy chwytu z modelu -> podejscie -> zacisk -> sloik (sciezka z nagrania) -> HOME. +import base64, json, sys, time +import arm_control as ac +from pinecone_bot.config import Config +from pinecone_bot.arm import WaypointArm +from pinecone_bot.camera import RealSenseCamera + +M = json.loads(MODEL) +J4 = M["joints"] +HOME = {"shoulder_pan": -5.45, "shoulder_lift": 88.3, "elbow_flex": 7.56, "wrist_flex": -87.87, "wrist_roll": 88.88} +# sciezka do sloika z nagrania (cykl 61.9-70 s), bez podstawy na poczatku (zostaje z chwytu) +JAR_UP = [ # (pan albo None = jak przy chwycie, lift, elbow, wrist, sekundy) + (None, 74.4, -63.3, -72.8, 1.2), (None, 36.7, -77.5, -72.9, 1.2), (None, -35.3, -78.8, -73.7, 1.6), + (-18.1, -38.5, -98.8, -74.0, 1.2), (-19.0, -57.8, -97.8, -84.7, 1.0), (-22.2, -60.8, -97.7, -84.6, 1.0)] +JAR_BACK = [(-21.8, -59.7, -76.0, -80.3, 1.0), (-21.9, -45.1, -33.3, -80.3, 1.0), (-21.8, -29.8, -3.1, -80.3, 1.0), + (-13.2, 60.1, 17.8, -80.2, 2.0)] +T0 = time.time() + + +def log(*a): + print(f"[{time.time() - T0:5.1f}s]", *a, file=sys.stderr, flush=True) + + +cfg = Config.load() +arm = ac.make_arm(port="/dev/robot-arm", max_relative_target=None) +arm.bus.connect() +cur = arm.bus.sync_read("Present_Position") +arm.bus.sync_write("Goal_Position", cur) +arm.bus.enable_torque() +ctl = WaypointArm(cfg, arm=arm) +cam = RealSenseCamera(cfg, depth=True, laser_power=360) +intr = cam.intrinsics + + +def pose(): + return arm.bus.sync_read("Present_Position") + + +def go(target, sec): + ctl._move(target, sec) + time.sleep(0.25) + cmd = dict(target) + for _ in range(3): # korekta calkujaca bledu serw + now = pose() + err = {j: target[j] - now[j] for j in target if j != "gripper"} + if all(abs(e) <= 0.8 for e in err.values()): + break + cmd = {j: (cmd[j] + max(-6.0, min(6.0, err[j])) if j in err and abs(err[j]) > 0.8 else cmd[j]) for j in cmd} + ctl._move(cmd, 0.4) + time.sleep(0.2) + return pose() + + +def feats(p): + x, z = p[0], p[2] + f = [1.0, x, z] + ([x * x, z * z, x * z] if M["quad"] else []) + return np.array(f) + + +def snapshot(): + for _ in range(6): + bgr, depth = cam.read() + valid = (depth > 0.1) & (depth < 1.5) + P = xyz_image(depth, intr) + n, d = fit_ground(P, valid) + H = np.where(valid, P @ n + d, 0.0) + hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV) + grip = (cv2.inRange(hsv, (88, 100, 90), (120, 255, 255)) > 0) | ((H > 0.08) & valid) + grip = cv2.dilate(grip.astype(np.uint8), np.ones((15, 15), np.uint8)) > 0 + low = ((H > 0.012) & (H < 0.08) & valid & ~grip).astype(np.uint8) + low = cv2.morphologyEx(low, cv2.MORPH_OPEN, np.ones((5, 5), np.uint8)) + num, lab, st, cent = cv2.connectedComponentsWithStats(low, connectivity=8) + cones = [] + for i in range(1, num): + a = int(st[i, cv2.CC_STAT_AREA]) + if 250 <= a <= 9000 and not (cent[i][1] > 380 and 330 < cent[i][0] < 580): # kabel serwa pod chwytakiem + cones.append({"uv": (float(cent[i][0]), float(cent[i][1])), "p": P[lab == i].mean(0)}) + vis = cv2.applyColorMap(cv2.convertScaleAbs(np.clip(H, 0, 0.06), alpha=255 / 0.06), cv2.COLORMAP_JET) + return cones, bgr, vis + + +result, img = "BRAK", None +try: + go({**HOME, "gripper": 30.0}, 3.0) + cones, bgr, vis = snapshot() + if not cones: + raise StopIteration + lo, hi = np.array(M["p_min"]), np.array(M["p_max"]) + # szyszki w obszarze, na ktorym model byl uczony (+3 cm zapasu), najblizsza kamerze + inside = [c for c in cones if np.all(c["p"][[0, 2]] >= lo[[0, 2]] - 0.06) and np.all(c["p"][[0, 2]] <= hi[[0, 2]] + 0.06)] + log(f"szyszki {len(cones)}, w obszarze uczenia {len(inside)}") + if not inside: + result = "POZA OBSZAREM UCZENIA" + raise StopIteration + c = min(inside, key=lambda c: c["p"][2]) + q = feats(c["p"]) @ np.array(M["W"]) + q = np.clip(q, np.array(M["q_min"]), np.array(M["q_max"])) # tylko pozy z zakresu uczenia + grasp = {j: float(q[k]) for k, j in enumerate(J4)} + grasp["wrist_roll"] = M["wrist_roll"] + pre = {j: grasp[j] + M["approach_delta"][k] for k, j in enumerate(J4)} + pre["wrist_roll"] = M["wrist_roll"] + log(f"szyszka uv {np.round(c['uv'], 0)} p {np.round(c['p'], 3)} -> chwyt {({k: round(v, 1) for k, v in grasp.items()})}") + for img_ in (bgr, vis): + cv2.circle(img_, (int(c["uv"][0]), int(c["uv"][1])), 16, (255, 255, 255), 2) + img = np.hstack((cv2.resize(bgr, (320, 240)), cv2.resize(vis, (320, 240)))) + go({**pre, "gripper": 100.0}, 2.0) + go({**grasp, "gripper": 100.0}, 1.5) + ctl._move({"gripper": 0.0}, 1.0) + time.sleep(0.4) + g = pose()["gripper"] + held = g >= cfg.arm.empty_gripper_below + log(f"zacisk: chwytak {g:.1f} -> {'TRZYMA' if held else 'PUSTY'}") + hold = max(g - 4.0, 0.0) + ctl._move({"gripper": hold}, 0.3) + if not held: + result = "PUSTO" + go({**pre, "gripper": 100.0}, 1.5) + raise StopIteration + for pan, lift, elbow, wrist, sec in JAR_UP: + go({"shoulder_pan": grasp["shoulder_pan"] if pan is None else pan, "shoulder_lift": lift, + "elbow_flex": elbow, "wrist_flex": wrist, "wrist_roll": M["wrist_roll"], "gripper": hold}, sec * 1.5) + ctl._move({"gripper": 100.0}, 0.8) + time.sleep(0.5) + result = "DO SLOIKA" + for pan, lift, elbow, wrist, sec in JAR_BACK: + go({"shoulder_pan": pan, "shoulder_lift": lift, "elbow_flex": elbow, "wrist_flex": wrist, + "wrist_roll": M["wrist_roll"], "gripper": 60.0}, sec * 1.5) +except StopIteration: + pass +finally: + try: + go({**HOME, "gripper": 30.0}, 2.5) + finally: + arm.bus.disconnect(disable_torque=False) + cam.close() +log("RESULT", result) +if img is not None: + print(base64.b64encode(cv2.imencode(".jpg", img)[1].tobytes()).decode()) diff --git a/tools/live_grasp/pick_loop.py b/tools/live_grasp/pick_loop.py new file mode 100644 index 0000000..4eeb9e4 --- /dev/null +++ b/tools/live_grasp/pick_loop.py @@ -0,0 +1,171 @@ +# Zbieranie z modelu z uczenia (doklejane za percept.py; MODEL = json z fit.py). +# HOME -> zdjecie glebi -> szyszka -> stawy chwytu z modelu -> podejscie -> zacisk -> sloik (sciezka z nagrania) -> HOME. +import base64, json, sys, time +import arm_control as ac +from pinecone_bot.config import Config +from pinecone_bot.arm import WaypointArm +from pinecone_bot.camera import RealSenseCamera + +M = json.loads(MODEL) +J4 = M["joints"] +HOME = {"shoulder_pan": -5.45, "shoulder_lift": 88.3, "elbow_flex": 7.56, "wrist_flex": -87.87, "wrist_roll": 88.88} +# sciezka do sloika z nagrania (cykl 61.9-70 s), bez podstawy na poczatku (zostaje z chwytu) +JAR_UP = [ # (pan albo None = jak przy chwycie, lift, elbow, wrist, sekundy) + (None, 74.4, -63.3, -72.8, 1.2), (None, 36.7, -77.5, -72.9, 1.2), (None, -35.3, -78.8, -73.7, 1.6), + (-18.1, -38.5, -98.8, -74.0, 1.2), (-19.0, -57.8, -97.8, -84.7, 1.0), (-22.2, -60.8, -97.7, -84.6, 1.0)] +JAR_BACK = [(-21.8, -59.7, -76.0, -80.3, 1.0), (-21.9, -45.1, -33.3, -80.3, 1.0), (-21.8, -29.8, -3.1, -80.3, 1.0), + (-13.2, 60.1, 17.8, -80.2, 2.0)] +T0 = time.time() + + +def log(*a): + print(f"[{time.time() - T0:5.1f}s]", *a, file=sys.stderr, flush=True) + + +cfg = Config.load() +arm = ac.make_arm(port="/dev/robot-arm", max_relative_target=None) +arm.bus.connect() +cur = arm.bus.sync_read("Present_Position") +arm.bus.sync_write("Goal_Position", cur) +arm.bus.enable_torque() +ctl = WaypointArm(cfg, arm=arm) +cam = RealSenseCamera(cfg, depth=True, laser_power=360) +intr = cam.intrinsics + + +def pose(): + return arm.bus.sync_read("Present_Position") + + +def go(target, sec): + ctl._move(target, sec) + time.sleep(0.25) + cmd = dict(target) + for _ in range(3): # korekta calkujaca bledu serw + now = pose() + err = {j: target[j] - now[j] for j in target if j != "gripper"} + if all(abs(e) <= 0.8 for e in err.values()): + break + cmd = {j: (cmd[j] + max(-6.0, min(6.0, err[j])) if j in err and abs(err[j]) > 0.8 else cmd[j]) for j in cmd} + ctl._move(cmd, 0.4) + time.sleep(0.2) + return pose() + + +def feats(p): + x, z = p[0], p[2] + f = [1.0, x, z] + ([x * x, z * z, x * z] if M["quad"] else []) + return np.array(f) + + +def snapshot(): + for _ in range(6): + bgr, depth = cam.read() + valid = (depth > 0.1) & (depth < 1.5) + P = xyz_image(depth, intr) + n, d = fit_ground(P, valid) + H = np.where(valid, P @ n + d, 0.0) + hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV) + grip = (cv2.inRange(hsv, (88, 100, 90), (120, 255, 255)) > 0) | ((H > 0.08) & valid) + grip = cv2.dilate(grip.astype(np.uint8), np.ones((15, 15), np.uint8)) > 0 + low = ((H > 0.012) & (H < 0.08) & valid & ~grip).astype(np.uint8) + low = cv2.morphologyEx(low, cv2.MORPH_OPEN, np.ones((5, 5), np.uint8)) + num, lab, st, cent = cv2.connectedComponentsWithStats(low, connectivity=8) + cones = [] + for i in range(1, num): + a = int(st[i, cv2.CC_STAT_AREA]) + if 250 <= a <= 9000 and not (cent[i][1] > 380 and 330 < cent[i][0] < 580): # kabel serwa pod chwytakiem + cones.append({"uv": (float(cent[i][0]), float(cent[i][1])), "p": P[lab == i].mean(0)}) + vis = cv2.applyColorMap(cv2.convertScaleAbs(np.clip(H, 0, 0.06), alpha=255 / 0.06), cv2.COLORMAP_JET) + return cones, bgr, vis + + + +N_TRIES = 10 +bias = np.zeros(3) # poprawka celu w ukladzie kamery HOME [m], uczona z przepchniec +wrist_extra = 0.0 # glebiej, gdy szyszka nieruszona +sheet, result = [], "NIE ZLAPANA" +qmin = np.array(M["q_min"]) - 8.0 +qmax = np.array(M["q_max"]) + 8.0 + + +def cone_near(cones, p, max_d=0.12): + c = min(cones, key=lambda c: np.linalg.norm(c["p"] - p), default=None) + return c if c is not None and np.linalg.norm(c["p"] - p) < max_d else None + + +try: + go({**HOME, "gripper": 30.0}, 3.0) + cones, bgr, vis = snapshot() + target = max(cones, key=lambda c: c["p"][0] * 0 - c["p"][2]) if cones else None # najblizsza kamerze + if target is None: + raise StopIteration + p_track = target["p"] + for k in range(N_TRIES): + c = cone_near(cones, p_track) + if c is None: + result = "SZYSZKA ZNIKNELA Z KADRU" + break + p_track = c["p"] + aim = c["p"] + bias + q = np.clip(feats(aim) @ np.array(M["W"]), qmin, qmax) + grasp = {j: float(q[i]) for i, j in enumerate(J4)} + grasp["wrist_flex"] += wrist_extra + grasp["wrist_roll"] = M["wrist_roll"] + pre = {j: grasp[j] + M["approach_delta"][i] for i, j in enumerate(J4)} + pre["wrist_roll"] = M["wrist_roll"] + log(f"proba {k + 1}: szyszka p {np.round(c['p'], 3)} poprawka {np.round(bias * 100, 1)} cm, wrist+{wrist_extra:.0f} -> " + f"{({j: round(v, 1) for j, v in grasp.items()})}") + im = bgr.copy() + cv2.circle(im, (int(c["uv"][0]), int(c["uv"][1])), 16, (255, 255, 255), 2) + cv2.putText(im, f"proba {k + 1}", (8, 30), 0, 1.0, (0, 255, 255), 2) + sheet.append(cv2.resize(im, (320, 240))) + go({**pre, "gripper": 100.0}, 2.0) + go({**grasp, "gripper": 100.0}, 1.5) + ctl._move({"gripper": 0.0}, 1.0) + time.sleep(0.4) + g = pose()["gripper"] + held = g >= cfg.arm.empty_gripper_below + log(f" zacisk: chwytak {g:.1f} -> {'TRZYMA' if held else 'PUSTY'}") + if held: + hold = max(g - 4.0, 0.0) + ctl._move({"gripper": hold}, 0.3) + for pan, lift, elbow, wrist, sec in JAR_UP: + go({"shoulder_pan": grasp["shoulder_pan"] if pan is None else pan, "shoulder_lift": lift, + "elbow_flex": elbow, "wrist_flex": wrist, "wrist_roll": M["wrist_roll"], "gripper": hold}, sec * 1.5) + ctl._move({"gripper": 100.0}, 0.8) + time.sleep(0.5) + for pan, lift, elbow, wrist, sec in JAR_BACK: + go({"shoulder_pan": pan, "shoulder_lift": lift, "elbow_flex": elbow, "wrist_flex": wrist, + "wrist_roll": M["wrist_roll"], "gripper": 60.0}, sec * 1.5) + result = f"ZLAPANA w probie {k + 1}, do sloika" + break + go({**pre, "gripper": 100.0}, 1.2) + go({**HOME, "gripper": 30.0}, 2.0) + cones, bgr, vis = snapshot() + after = cone_near(cones, p_track) + if after is None: + result = "SZYSZKA ZNIKNELA Z KADRU" + break + push = after["p"] - p_track + if np.linalg.norm(push) > 0.01: + bias = bias + 0.7 * push + log(f" przepchnieta o {np.round(push * 100, 1)} cm -> poprawka {np.round(bias * 100, 1)} cm") + else: + wrist_extra = min(wrist_extra + 3.0, 12.0) + log(f" nieruszona -> glebiej, wrist +{wrist_extra:.0f}") + p_track = after["p"] +except StopIteration: + pass +finally: + try: + go({**HOME, "gripper": 30.0}, 2.5) + finally: + arm.bus.disconnect(disable_torque=False) + cam.close() +log("RESULT", result) +if sheet: + while len(sheet) % 2: + sheet.append(np.zeros_like(sheet[0])) + img = np.vstack([np.hstack(sheet[i:i + 2]) for i in range(0, len(sheet), 2)]) + print(base64.b64encode(cv2.imencode(".jpg", img)[1].tobytes()).decode()) diff --git a/tools/live_grasp/rec.py b/tools/live_grasp/rec.py new file mode 100644 index 0000000..ddae6ef --- /dev/null +++ b/tools/live_grasp/rec.py @@ -0,0 +1,40 @@ +# Nagranie ruchu reka: torque off, pozycje 30 Hz + zdjecie co 0.5 s. Wyjscie na stdout (linie POS/IMG). +import base64, json, sys, time +import arm_control as ac +from pinecone_bot.config import Config +from pinecone_bot.camera import RealSenseCamera +DUR = float(__import__("os").environ.get("REC_SECONDS", "120")) +cfg = Config.load() +arm = ac.make_arm(port="/dev/robot-arm", max_relative_target=None) +arm.bus.connect() +arm.bus.disable_torque() +cam = RealSenseCamera(cfg, depth=True, laser_power=360) +intr = cam.intrinsics +print("START", time.time(), file=sys.stderr, flush=True) +t0 = time.time(); i = 0 +try: + while time.time() - t0 < DUR: + bgr, depth = cam.read() + try: + pos = arm.bus.sync_read("Present_Position") + except Exception: + continue + t = time.time() - t0 + print("POS " + json.dumps({"t": round(t, 3), **{k: round(v, 2) for k, v in pos.items()}}), flush=True) + if i % 15 == 0: + valid = (depth > 0.1) & (depth < 1.5) + g = fit_ground(xyz_image(depth, intr), valid) + if g is not None: + H = np.where(valid, xyz_image(depth, intr) @ g[0] + g[1], -1.0) + vis = cv2.applyColorMap(cv2.convertScaleAbs(np.clip(H, 0, 0.06), alpha=255.0 / 0.06), cv2.COLORMAP_JET) + vis[H < 0] = 0 + else: + vis = np.zeros_like(bgr) + img = np.hstack((cv2.resize(bgr, (320, 240)), cv2.resize(vis, (320, 240)))) + cv2.putText(img, f"{t:.1f}s", (6, 20), 0, 0.6, (255, 255, 255), 2) + print("IMG %.2f %s" % (t, base64.b64encode(cv2.imencode(".jpg", img, [cv2.IMWRITE_JPEG_QUALITY, 70])[1].tobytes()).decode()), flush=True) + i += 1 +finally: + cam.close() + arm.bus.disconnect(disable_torque=False) + print("KONIEC", file=sys.stderr, flush=True) diff --git a/tools/live_grasp/replay.py b/tools/live_grasp/replay.py new file mode 100644 index 0000000..c48c317 --- /dev/null +++ b/tools/live_grasp/replay.py @@ -0,0 +1,38 @@ +# Odtworzenie nagranego cyklu (CYCLE = lista pozycji z czasem), tempo / SLOW. Chwytak: przy zacisku (odczyt < 12.5) cel = odczyt - 6. +import json, sys, time +import arm_control as ac +from pinecone_bot.config import Config +from pinecone_bot.arm import WaypointArm +C = json.loads(CYCLE) +SLOW = 2.0 +J = ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"] +cfg = Config.load() +arm = ac.make_arm(port="/dev/robot-arm", max_relative_target=None) +arm.bus.connect() +cur = arm.bus.sync_read("Present_Position") +arm.bus.sync_write("Goal_Position", cur) +arm.bus.enable_torque() +ctl = WaypointArm(cfg, arm=arm) +def cmd(p): + q = {j: p[j] for j in J} + if q["gripper"] < 12.5: + q["gripper"] = max(0.0, q["gripper"] - 6.0) + return q +try: + print("dojazd do startu cyklu (3 s)", file=sys.stderr, flush=True) + ctl._move(cmd(C[0]), 3.0) + t0 = time.time() + for p in C: + target_t = (p["t"] - C[0]["t"]) * SLOW + wait = t0 + target_t - time.time() + if wait > 0: + time.sleep(wait) + arm.send_action({f"{j}.pos": v for j, v in cmd(p).items()}) + if abs(p["t"] - 61.9) < 0.05 or abs(p["t"] - 66.5) < 0.05: + now = arm.bus.sync_read("Present_Position") + print(f"t {p['t']:.1f}: chwytak {now['gripper']:.1f}", file=sys.stderr, flush=True) + time.sleep(0.8) + now = arm.bus.sync_read("Present_Position") + print("koniec:", {k: round(v, 1) for k, v in now.items()}, file=sys.stderr) +finally: + arm.bus.disconnect(disable_torque=False) diff --git a/tools/live_grasp/servo.py b/tools/live_grasp/servo.py new file mode 100644 index 0000000..0941a13 --- /dev/null +++ b/tools/live_grasp/servo.py @@ -0,0 +1,249 @@ +# Serwo wizyjne z glebi, samo ramie. Doklejane za percept.py, wysylane na Pi przez stdin. +# STATE (json) podstawia runner. Log na stderr, na koncu "STATE {...}"; stdout: jpg ostatniej klatki. +import base64, json, sys, time +import arm_control as ac +from pinecone_bot.config import Config +from pinecone_bot.arm import WaypointArm +from pinecone_bot.camera import RealSenseCamera + +S = json.loads(STATE) +JOINTS = ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex"] +LIM = {"shoulder_pan": (-45, 30), "shoulder_lift": (55, 88.3), "elbow_flex": (-45, 40), "wrist_flex": (-100, -30)} +HOME = {"shoulder_pan": -5.45, "shoulder_lift": 88.3, "elbow_flex": 7.56, "wrist_flex": -87.87, "wrist_roll": 88.88} +T0 = time.time() + + +def log(*a): + print(f"[{time.time() - T0:5.1f}s]", *a, file=sys.stderr, flush=True) + + +cfg = Config.load() +arm = ac.make_arm(port="/dev/robot-arm", max_relative_target=None) +for i in range(3): + try: + arm.bus.connect() + break + except Exception as e: + log("connect nieudany:", str(e).splitlines()[0]) + try: + arm.bus.port_handler.closePort() + except Exception: + pass + time.sleep(1.0) +cur = arm.bus.sync_read("Present_Position") +arm.bus.sync_write("Goal_Position", cur) +arm.bus.enable_torque() +ctl = WaypointArm(cfg, arm=arm) +cam = RealSenseCamera(cfg, depth=True) +intr = cam.intrinsics + + +def pose(): + return arm.bus.sync_read("Present_Position") + + +def go(target, sec=1.0): + """Ruch ~1 s, potem korekta calkujaca bledu serw (max 3 razy).""" + ctl._move(target, sec) + time.sleep(0.3) + cmd = dict(target) + for _ in range(3): + now = pose() + err = {j: target[j] - now[j] for j in target if j != "gripper"} + if all(abs(e) <= 0.8 for e in err.values()): + break + cmd = {j: (cmd[j] + max(-6.0, min(6.0, err[j])) if j in err and abs(err[j]) > 0.8 else cmd[j]) for j in cmd} + ctl._move(cmd, 0.4) + time.sleep(0.25) + return pose() + + +def clamp_joints(q): + return {j: max(LIM[j][0], min(LIM[j][1], v)) if j in LIM else v for j, v in q.items()} + + +last = {"bgr": None, "per": None, "cone": None} + + +def measure(prefer_uv, max_jump=90): + """-> (y [cm]: poziomo e1, poziomo e2, wysokosc koncowki; stozek; chwytak) albo None. + Sledzi jedna szyszke: bez przeskoku na inna (dalej niz max_jump px od poprzedniej = zgubiona).""" + for _ in range(3): + bgr, depth = cam.read() + per = perceive(depth, intr, bgr) + last["bgr"], last["per"] = bgr, per + if per is None or per["grip"] is None or not per["cones"]: + return None + cone = pick_cone(per, prefer_uv) + if prefer_uv is not None and np.hypot(cone["uv"][0] - prefer_uv[0], cone["uv"][1] - prefer_uv[1]) > max_jump: + return None + last["cone"] = cone + n, _ = per["ground"] + x = np.array([1.0, 0.0, 0.0]) + e1 = x - (x @ n) * n + e1 /= np.linalg.norm(e1) + e2 = np.cross(n, e1) + e = cone["p"] - per["grip"]["p"] + y = np.array([e @ e1, e @ e2, per["grip"]["h"]]) * 100.0 + return y, cone, per["grip"] + + +def residual(y, h_goal): + return y - np.array([S["off"][0], S["off"][1], h_goal]) + + +def solve(J, r, max_step=4.0): + J = np.asarray(J) + lam = 0.1 + dq = -J.T @ np.linalg.solve(J @ J.T + lam * np.eye(J.shape[0]), r) + m = np.abs(dq).max() + if m > max_step: + return dq * (max_step / m) + if 1e-6 < m < 1.2: # ponizej ~1 st serwo nie rusza (niskie P) + return dq * (1.2 / m) + return dq + + +def broyden(J, dq, dy): + J = np.asarray(J) + if np.abs(dq).max() < 1.5: + return J + return J + 0.3 * np.outer(dy - J @ dq, dq) / (dq @ dq) + + +def qvec(p): + return np.array([p[j] for j in JOINTS]) + + +result = "NIE UDALO SIE" +try: + go({**{j: cur[j] for j in JOINTS}, "gripper": 0.0}, 0.8) # zamkniety chwytak = jedna koncowka do sledzenia + m = measure(None) + if m is None: + raise SystemExit("brak szyszki albo chwytaka w glebi") + y, cone, grip = m + uv = cone["uv"] + log(f"start: blad poziomo ({y[0]:+.1f},{y[1]:+.1f}) cm, koncowka {y[2]:.1f} cm nad ziemia, szyszka h {cone['h']*100:.1f} cm") + # 1. Jakobian: proba kazdym stawem (jesli nie ma nauczonego) + J = None # proby na starcie kazdego uruchomienia (Jakobian zalezy od pozy) + if J is None: + J = np.zeros((3, 4)) + for i, j in enumerate(JOINTS): + p0 = pose() + step = -3.0 if j == "shoulder_lift" else 3.0 + p1 = go({**{k: p0[k] for k in JOINTS}, j: p0[j] + step, "gripper": 0.0}) + m1 = measure(uv) + dq = p1[j] - p0[j] + if m1 is None or abs(dq) < 0.8: + log(f"proba {j}: brak pomiaru albo staw nie ruszyl (dq {dq:.1f})") + continue + J[:, i] = (m1[0] - y) / dq + log(f"proba {j}: dq {dq:+.1f} -> dy {np.round(m1[0] - y, 2)} cm") + y, uv = m1[0], m1[1]["uv"] + S["J"] = J.round(3).tolist() + # 2. petla: 1 s ruchu, stop, glebia, nastepny krok + phase, h_goal = "A", S["h_above"] + lost = 0 + for it in range(S["max_iter"]): + r = residual(y, h_goal) + log(f"{phase}{it}: blad poziomo ({r[0]:+.1f},{r[1]:+.1f}) wysokosc {r[2]:+.1f} cm") + if np.abs(r[:2]).max() <= S["tol_xy"] and abs(r[2]) <= S["tol_h"]: + if phase == "A": + # 3. nad szyszka: otworz, zejdz na slepo z Jakobianu, zacisnij + p_above = pose() + ctl._move({"gripper": 100.0}, 0.8) + dq = solve(J, np.array([0.0, 0.0, y[2] - S["h_grasp"]]), max_step=12.0) + log(f"schodze {y[2]:.1f} -> {S['h_grasp']:.1f} cm: dq {np.round(dq, 1)}") + target = clamp_joints({j: p_above[j] + dq[k] for k, j in enumerate(JOINTS)}) + go({**target, "gripper": 100.0}, 1.5) + ctl._move({"gripper": 0.0}, 1.0) + time.sleep(0.4) + g = pose()["gripper"] + held = g >= cfg.arm.empty_gripper_below + log(f"zacisk: chwytak {g:.1f} -> {'TRZYMA' if held else 'PUSTY'}") + hold = max(g - 3.0, 0.0) + ctl._move({"gripper": hold}, 0.3) + go({**{j: p_above[j] for j in JOINTS}, "gripper": hold}, 1.5) + cone_before = cone + m2 = measure(uv) + still = None + if last["per"] is not None: + still = min(last["per"]["cones"], key=lambda c: np.linalg.norm(c["p"] - cone_before["p"]), default=None) + if still is not None and np.linalg.norm(still["p"] - cone_before["p"]) > 0.06: + still = None + if held and still is None: + result = "PODNIESIONA" + S["ok"] = S.get("ok", 0) + 1 + break + S["fails"] = S.get("fails", 0) + 1 + if still is not None: + push = (still["p"] - cone_before["p"]) * 100.0 + n, _ = last["per"]["ground"] + x = np.array([1.0, 0.0, 0.0]); e1 = x - (x @ n) * n; e1 /= np.linalg.norm(e1); e2 = np.cross(n, e1) + ph = np.array([push @ e1, push @ e2]) + if np.linalg.norm(ph) < 1.0: + S["h_grasp"] = round(max(-2.0, S["h_grasp"] - 0.7), 2) + log(f"szyszka nieruszona -> nizej: h_grasp {S['h_grasp']} cm") + else: + S["off"] = [round(S["off"][0] - 0.5 * ph[0], 2), round(S["off"][1] - 0.5 * ph[1], 2)] + log(f"szyszka popchnieta o ({ph[0]:+.1f},{ph[1]:+.1f}) cm -> przesuniecie celu {S['off']}") + else: # nie widac: lezy pod chwytakiem -> szczeki zamknely sie nad nia + S["h_grasp"] = round(max(-2.0, S["h_grasp"] - 0.8), 2) + log(f"szyszka pod chwytakiem (zaslonieta) -> nizej: h_grasp {S['h_grasp']} cm") + if held: + ctl._move({"gripper": 100.0}, 0.8) + ctl._move({"gripper": 0.0}, 0.8) + # odjedz w gore, zeby zobaczyc szyszke i sprobowac jeszcze raz + go({**{j: p_above[j] for j in JOINTS}, "shoulder_lift": min(LIM["shoulder_lift"][1], p_above["shoulder_lift"] + 4), "gripper": 0.0}) + m = measure(None) + if m is None: + log("po probie nie widze szyszki") + break + y, cone, grip = m + uv = cone["uv"] + continue + p0 = pose() + # stawy na granicy zakresu wypadaja z rozwiazania (inaczej obciety krok = zero ruchu) + active = list(range(4)) + for _ in range(4): + dq = np.zeros(4) + dq[active] = solve(np.asarray(J)[:, active], r) + blocked = [k for k in active if (p0[JOINTS[k]] + dq[k] > LIM[JOINTS[k]][1] - 0.3 and dq[k] > 0) + or (p0[JOINTS[k]] + dq[k] < LIM[JOINTS[k]][0] + 0.3 and dq[k] < 0)] + if not blocked: + break + active = [k for k in active if k not in blocked] + if not active: + break + target = clamp_joints({j: p0[j] + dq[k] for k, j in enumerate(JOINTS)}) + log(f" krok {np.round(dq, 1)} (stawy {[JOINTS[k][:5] for k in active]})") + p1 = go({**target, "gripper": 0.0}) + dq_act = qvec(p1) - qvec(p0) + m = measure(uv) + if m is None: + y = y + J @ dq_act # szyszka zaslonieta: przewidywanie z Jakobianu + lost = lost + 1 + log(f" krok {np.round(dq_act, 1)}: szyszka zaslonieta ({lost}), przewiduje blad {np.round(residual(y, h_goal), 1)}") + if lost > 4: + break + continue + lost = 0 + J = broyden(J, dq_act, m[0] - y) + y, cone, grip = m + uv = cone["uv"] + S["J"] = np.asarray(J).round(3).tolist() +finally: + try: + g = pose()["gripper"] + go({**HOME, "gripper": g}, 2.0) + if result == "PODNIESIONA": + log("HOME z szyszka - zabierz ja (4 s)") + time.sleep(4.0) + ctl._move({"gripper": 100.0}, 0.8) + finally: + arm.bus.disconnect(disable_torque=False) + cam.close() +log("RESULT", result) +print("STATE " + json.dumps(S), file=sys.stderr) +if last["bgr"] is not None and last["per"] is not None: + print(base64.b64encode(cv2.imencode(".jpg", debug_image(last["bgr"], last["per"], last["cone"]))[1].tobytes()).decode()) diff --git a/tools/live_grasp/snap_only.py b/tools/live_grasp/snap_only.py new file mode 100644 index 0000000..7e3cf3a --- /dev/null +++ b/tools/live_grasp/snap_only.py @@ -0,0 +1,31 @@ +import base64, sys, json +import arm_control as ac +from pinecone_bot.config import Config +from pinecone_bot.camera import RealSenseCamera +from pinecone_bot.detector import HsvConeDetector +cfg = Config.load() +arm = ac.make_arm(port="/dev/robot-arm", max_relative_target=None) +arm.bus.connect() +pos = arm.bus.sync_read("Present_Position") +tq = arm.bus.sync_read("Torque_Enable", normalize=False) +arm.bus.disconnect(disable_torque=False) +print("POSE", json.dumps({k: round(v, 1) for k, v in pos.items()}), "torque", sum(tq.values()), file=sys.stderr) +cam = RealSenseCamera(cfg, depth=True, laser_power=360) +try: + for _ in range(5): + bgr, depth = cam.read() +finally: + cam.close() +intr = cam.intrinsics +valid = (depth > 0.1) & (depth < 1.5) +P = xyz_image(depth, intr) +n, d = fit_ground(P, valid) +H = np.where(valid, P @ n + d, -1.0) +print("mean BGR", bgr.reshape(-1, 3).mean(0).round(0), file=sys.stderr) +for c in HsvConeDetector(cfg.detector).detect(bgr): + print(f"HSV px={c.px:.0f} py={c.py:.0f} area={c.area:.0f}", file=sys.stderr) + x, y, w, h = c.bbox + cv2.rectangle(bgr, (x, y), (x + w, y + h), (0, 0, 255), 2) +vis = cv2.applyColorMap(cv2.convertScaleAbs(np.clip(H, 0, 0.06), alpha=255.0 / 0.06), cv2.COLORMAP_JET) +vis[H < 0] = 0 +print(base64.b64encode(cv2.imencode(".jpg", np.hstack((cv2.resize(bgr, (480, 360)), cv2.resize(vis, (480, 360)))))[1].tobytes()).decode()) diff --git a/tools/live_grasp/state.json b/tools/live_grasp/state.json new file mode 100644 index 0000000..ef51b5c --- /dev/null +++ b/tools/live_grasp/state.json @@ -0,0 +1 @@ +{"k": 9.3, "align_x": 412, "align_y": 230, "tol_px": 12, "reach_py": 150, "jaw_box": [425, 175, 545, 480], "body_y": 330, "goff_elbow": -5.0, "goff_wrist": 11.0, "tries": 14, "ok": 0, "log": [{"pan": -5.6, "aligned": [407, 180], "gripper": 1.6, "result": "PUSTO"}, {"pan": -5.6, "lift": 82.3, "aligned": [405, 238], "gripper": 1.7, "result": "PUSTO"}, {"pan": -5.6, "lift": 83.8, "aligned": [406, 221], "gripper": 1.7, "shift": [-7, -43], "result": "PUSTO"}, {"pan": -5.6, "lift": 85.9, "aligned": [404, 193], "gripper": 1.4, "shift": [4, -27], "result": "PUSTO"}, {"pan": -5.6, "lift": 86.3, "aligned": [402, 184], "gripper": 1.4, "shift": [5, -50], "result": "PUSTO"}, {"pan": -5.8, "lift": 86.3, "aligned": [407, 150], "gripper": 1.4, "shift": [3, -19], "result": "PUSTO"}, {"pan": -6.0, "lift": 86.4, "aligned": [409, 146], "gripper": 1.4, "shift": [4, -16], "result": "PUSTO"}, {"pan": -6.2, "lift": 86.7, "aligned": [409, 143], "gripper": 1.4, "shift": [3, -13], "result": "PUSTO"}, {"pan": -5.6, "lift": 86.8, "aligned": [405, 142], "gripper": 1.4, "shift": [6, -15], "result": "PUSTO"}, {"pan": -7.8, "lift": 79.8, "aligned": [406, 218], "gripper": 1.4, "shift": [-11, -34], "result": "PUSTO"}, {"pan": -8.4, "lift": 78.0, "aligned": [404, 215], "roll": 0.0, "bbox": [44, 44], "gripper": 1.4, "shift": null, "result": "PUSTO"}, {"pan": -10.9, "lift": 80.8, "aligned": [406, 197], "roll": -90.0, "bbox": [80, 51], "gripper": 1.4, "shift": [15, 6], "result": "PUSTO"}, {"pan": -19.7, "lift": 87.1, "aligned": [402, 240], "roll": 0.0, "bbox": [34, 45], "gripper": 1.7, "shift": [85, -91], "result": "PUSTO"}, {"pan": -12.7, "lift": 81.6, "aligned": [423, 221], "roll": 0.0, "bbox": [20, 37], "gripper": 1.6, "shift": null, "result": "PUSTO"}], "ky": 11.09, "tol_py": 15, "lift_min": 60.0, "lift_max": 88.3, "roll_turn": -90.0, "wide_ratio": 1.3} diff --git a/tools/live_grasp/step.py b/tools/live_grasp/step.py new file mode 100644 index 0000000..6b57497 --- /dev/null +++ b/tools/live_grasp/step.py @@ -0,0 +1,38 @@ +# Wysylany na Pi przez stdin (python -). ARGS podstawiane przez step.sh. +import base64, json, sys, time, cv2 +import arm_control as ac +from pinecone_bot.config import Config +from pinecone_bot.arm import WaypointArm +from pinecone_bot.camera import RealSenseCamera +from pinecone_bot.detector import HsvConeDetector +A = json.loads(ARGS) +cfg = Config.load() +cfg.arm.port = "/dev/robot-arm" +arm = ac.make_arm(port=cfg.arm.port, max_relative_target=None) +arm.bus.connect() +cur = arm.bus.sync_read("Present_Position") +arm.bus.sync_write("Goal_Position", cur) +arm.bus.enable_torque() +ctl = WaypointArm(cfg, arm=arm) +target = dict(A.get("abs", {})) +for j, d in A.get("rel", {}).items(): + target[j] = cur[j] + d +if target: + ctl._move(target, float(A.get("sec", 1.0))) + time.sleep(0.4) +pose = arm.bus.sync_read("Present_Position") +arm.bus.disconnect(disable_torque=False) +print("POSE " + json.dumps({k: round(v, 1) for k, v in pose.items()}), file=sys.stderr) +if A.get("snap", True): + cam = RealSenseCamera(cfg, depth=False) + try: + for _ in range(3): + bgr, _ = cam.read() + finally: + cam.close() + dets = HsvConeDetector(cfg.detector).detect(bgr) + for d in dets: + print(f"DET px={d.px:.0f} py={d.py:.0f} area={d.area:.0f} bbox={d.bbox} partial={d.partial}", file=sys.stderr) + img = HsvConeDetector.draw_debug(bgr, dets) + cv2.drawMarker(img, (421, 336), (255, 0, 255), cv2.MARKER_CROSS, 30, 2) + print(base64.b64encode(cv2.imencode(".jpg", img)[1].tobytes()).decode()) diff --git a/tools/live_grasp/teach_clean.py b/tools/live_grasp/teach_clean.py new file mode 100644 index 0000000..4a703cc --- /dev/null +++ b/tools/live_grasp/teach_clean.py @@ -0,0 +1,162 @@ +# Petla uczenia (doklejane za percept.py, uruchamiane na Pi przez nohup, zapis do /tmp/teach.jsonl). +# Cykl: HOME (torque) -> zdjecie glebi szyszek -> chwytak otwarty szeroko = "twoja kolej" -> torque off +# -> czlowiek chwyta reka (zamkniecie chwytaka = zapis pozy chwytu) -> wrzuca do sloika, otwiera +# -> po 5 s robot wraca do HOME i widzi, ktorej szyszki brakuje. +import base64, json, sys, time +import arm_control as ac +from pinecone_bot.config import Config +from pinecone_bot.arm import WaypointArm +from pinecone_bot.camera import RealSenseCamera + +MAX_SAMPLES, MAX_SECONDS = 12, 1200 +HOME = {"shoulder_pan": -5.45, "shoulder_lift": 88.3, "elbow_flex": 7.56, "wrist_flex": -87.87, "wrist_roll": 88.88} +J = ["shoulder_pan", "shoulder_lift", "elbow_flex", "wrist_flex", "wrist_roll", "gripper"] +OUT = open("/tmp/teach.jsonl", "a") +T0 = time.time() + + +def emit(kind, **kw): + OUT.write(json.dumps({"kind": kind, "t": round(time.time() - T0, 2), **kw}) + "\n") + OUT.flush() + + +def log(*a): + print(f"[{time.time() - T0:6.1f}s]", *a, file=sys.stderr, flush=True) + + +cfg = Config.load() +arm = ac.make_arm(port="/dev/robot-arm", max_relative_target=None) +arm.bus.connect() +ctl = WaypointArm(cfg, arm=arm) +cam = RealSenseCamera(cfg, depth=True, laser_power=360) +intr = cam.intrinsics + + +def pose(): + return arm.bus.sync_read("Present_Position") + + +def torque_on_here(): + cur = pose() + arm.bus.sync_write("Goal_Position", cur) + arm.bus.enable_torque() + ctl._last_cmd = dict(cur) + + +def go_home(): + ctl._move({**HOME, "gripper": 30.0}, 3.0) + time.sleep(0.3) + cmd = dict(HOME) + for _ in range(4): + now = pose() + err = {j: HOME[j] - now[j] for j in HOME} + if all(abs(e) <= 0.6 for e in err.values()): + break + cmd = {j: cmd[j] + max(-5.0, min(5.0, err[j])) if abs(err[j]) > 0.6 else cmd[j] for j in cmd} + ctl._move({**cmd, "gripper": 30.0}, 0.5) + time.sleep(0.25) + return pose() + + +def home_snapshot(): + """Chwytak w gore z kadru nie jest potrzebny: w HOME szczeki sa na dole obrazu (v > 390 odrzucane).""" + for _ in range(6): + bgr, depth = cam.read() + valid = (depth > 0.1) & (depth < 1.5) + P = xyz_image(depth, intr) + g = fit_ground(P, valid) + if g is None: + return [], bgr, None + n, d = g + H = np.where(valid, P @ n + d, 0.0) + # chwytak w HOME: niebieski PLA (przy swietle) + wszystko wyzej niz 8 cm, powiekszone; reszta kadru (takze dol) zostaje + hsv = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV) + grip = (cv2.inRange(hsv, (88, 100, 90), (120, 255, 255)) > 0) | ((H > 0.08) & valid) + grip = cv2.dilate(grip.astype(np.uint8), np.ones((15, 15), np.uint8)) > 0 + tall = ((H > 0.08) & valid & ~(cv2.inRange(hsv, (88, 100, 90), (120, 255, 255)) > 0)).astype(np.uint8) + tall = cv2.morphologyEx(tall, cv2.MORPH_OPEN, np.ones((7, 7), np.uint8)) + HAND["seen"] = int(tall[:300].sum()) > 3000 # wysoko nad ziemia, nie niebieskie, w gornej czesci kadru = reka + low = ((H > 0.012) & (H < 0.08) & valid & ~grip).astype(np.uint8) + low = cv2.morphologyEx(low, cv2.MORPH_OPEN, np.ones((5, 5), np.uint8)) + num, lab, st, cent = cv2.connectedComponentsWithStats(low, connectivity=8) + cones = [] + for i in range(1, num): + a = int(st[i, cv2.CC_STAT_AREA]) + if a < 250 or a > 9000 or (cent[i][1] > 380 and 330 < cent[i][0] < 580): # kabel serwa pod chwytakiem + continue + m = lab == i + cones.append({"uv": [round(float(cent[i][0]), 1), round(float(cent[i][1]), 1)], + "p": [round(float(x), 4) for x in P[m].mean(0)], "h": round(float(H[m].max()), 4), "area": a}) + vis = cv2.applyColorMap(cv2.convertScaleAbs(np.clip(np.where(valid, H, 0), 0, 0.06), alpha=255 / 0.06), cv2.COLORMAP_JET) + vis[grip] = (vis[grip] * 0.4).astype(np.uint8) + for c in cones: + cv2.circle(vis, (int(c["uv"][0]), int(c["uv"][1])), 14, (255, 255, 255), 2) + img = np.hstack((cv2.resize(bgr, (320, 240)), cv2.resize(vis, (320, 240)))) + return cones, img, [round(float(x), 4) for x in n] + [round(float(d), 4)] + + +HAND = {"seen": False} +samples = 0 +try: + while samples < MAX_SAMPLES and time.time() - T0 < MAX_SECONDS: + # 1. HOME i zdjecie: czekaj na dokladnie 1 szyszke i brak reki, 2 zgodne zdjecia pod rzad + torque_on_here() + hp = go_home() + log("HOME, blad serw: " + str({j[:5]: round(hp[j] - HOME[j], 1) for j in HOME})) + prev, t_ask = None, 0.0 + while time.time() - T0 < MAX_SECONDS: + cones, img, ground = home_snapshot() + ok = len(cones) == 1 and not HAND["seen"] + if ok and prev is not None and np.linalg.norm(np.subtract(cones[0]["p"], prev)) < 0.01: + break + prev = cones[0]["p"] if ok else None + if time.time() - t_ask > 10: + log(f"czekam: szyszek {len(cones)}, reka {HAND['seen']} - poloz JEDNA szyszke, rece poza kadrem") + t_ask = time.time() + time.sleep(0.7) + emit("home", cones=cones, ground=ground, joints={j: round(hp[j], 2) for j in HOME}, + img=base64.b64encode(cv2.imencode(".jpg", img, [cv2.IMWRITE_JPEG_QUALITY, 70])[1].tobytes()).decode()) + log(f"HOME: 1 szyszka uv {cones[0]['uv']}") + # 2. sygnal: chwytak szeroko otwarty, torque off + ctl._move({"gripper": 100.0}, 0.6) + time.sleep(0.3) + arm.bus.disable_torque() + log("TWOJA KOLEJ: chwyc szyszke") + # 3. czekaj na zacisk (spadek ponizej 12.5), potem na puszczenie (> 16) + traj, state, q_grasp = [], "open", None + t_wait = time.time() + while time.time() - T0 < MAX_SECONDS: + try: + q = pose() + except Exception: + continue + traj.append({"t": round(time.time() - T0, 2), **{j: round(q[j], 2) for j in J}}) + away = max(abs(q[j] - HOME[j]) for j in ("shoulder_pan", "elbow_flex", "wrist_flex")) > 5.0 + closing = q["gripper"] < 25.0 and time.time() - t_wait > 1.0 and away + if state == "open" and closing: + # zacisk = chwytak < 25 i stabilny (+-2) przez 0.5 s (szyszka w szczekach, reka juz nie domyka) + recent = [p["gripper"] for p in traj if p["t"] >= traj[-1]["t"] - 0.5] + if len(recent) >= 10 and max(recent) - min(recent) < 2.0 and max(recent) < 25.0: + q_grasp = {j: round(q[j], 2) for j in J} + state = "closed" + log(f"ZACISK: {q_grasp}") + elif state == "closed" and q["gripper"] > q_grasp["gripper"] + 6.0: + log("PUSZCZONA - puszczaj ramie, za 5 s wracam do HOME") + break + time.sleep(0.033) + if q_grasp is None: + break + emit("grasp", q=q_grasp, traj=traj[-240:]) + samples += 1 + time.sleep(5.0) +finally: + try: + torque_on_here() + ctl._move({**HOME, "gripper": 30.0}, 3.0) + cones, img, ground = home_snapshot() + emit("home", cones=cones, ground=ground, + img=base64.b64encode(cv2.imencode(".jpg", img, [cv2.IMWRITE_JPEG_QUALITY, 70])[1].tobytes()).decode()) + finally: + arm.bus.disconnect(disable_torque=False) + cam.close() + log(f"KONIEC, probek {samples}")