RoboKit is a high-performance, general-purpose toolkit for versatile robotic tasks. It scales from one problem to thousands in parallel.

Thousands of robots solving in parallel.

Benchmarks

Bars are wall-clock time on the same task, baseline normalized to full width. Check the paper for benchmark details.

Optimization/Learning with Simulation

NIST Assembly RL A Franka puts six NIST board parts in place, one after another (3× speed).	Pen Spinning MPC A Sharpa hand spins a pen in its fingers.	Box Carrying MPC A G1 lifts and carries a box.
Rough Terrain RL A Go1 walks up and down stairs, following the green velocity command.	Cube Turning MPC A palm-up Wuji hand tumbles a cube through 90° turns.	Climbing MPC A G1 climbs a box on hands and knees.
Lift RL A YAM arm carries a cube to target positions (red dot).	Notebook Opening MPC Two XHands open a hinged ARCTIC notebook.	Jumping MPC A Go2 lands four 40 cm jumps on target footholds.

Optimization with Analytic Objectives

IK Multi-link targets, fixed or floating base.	Motion Planning Collision-free trajectories, offline or online.	Hand-Eye Calibration Camera pose from rendered robot masks.
Humanoid Retargeting Human motion capture to a humanoid.	Hand Retargeting Human hand motion to a robot hand.	Grasp Optimization Force-closure grasps for any hand or body.

How it works

Component	Role	Read more
Robot	Load URDF or MJCF models and compute batched, differentiable kinematics.	Robot
Objectives	Cost terms that define a task: pose matching, collision, limits, contacts.	Terms
Optimizers	Sampling and gradient methods that minimize the objectives in parallel.	Optimization
Acceleration	Fast geometry and rendering for contact-rich and calibration tasks.	Geometry
Distillation	Train policies on solver outputs for even faster inference.

Installation

Requires Python 3.8 or newer:

uv add robokit      # or: pip install robokit

PyTorch is optional. See Installation for source installs and extras.

Quick links

• Homepage
• Docs
• Quickstart
• Installation
• Development

Citation

@inproceedings{ye2026robokit,
  title     = {RoboKit: Accelerating Parallel Differentiable Optimization for Robot Learning},
  author    = {Ye, Jianglong and Peng, Xuanbin and Mc Gartoll, Conor and Jing, Changwei and Wei, Lai and Chen, Jiali and Zou, Xueyan and Wang, Xiaolong},
  booktitle = {Conference on Robot Learning (CoRL)},
  year      = {2026}
}