Agents that Act, Build, and Evolve.
An open research platform for agentic robotics.
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RoboAgentLab brings robot task execution and experimental development into a shared coding-agent workspace. Built on pi, the platform combines an agent loop and persistent sessions with robotics extensions. Downstream servers provide simulation, robot control, perception, planning, and policy inference; upstream skills explain how to compose these capabilities. The same interfaces are available to agent tool calls and generated Python programs.
| Role | Research focus |
|---|---|
| Act — Agent as Policy | Select behaviors, orchestrate tools, and write programs that execute robot tasks. |
| Build — Agent as Infrastructure | Develop and debug robotics systems, reproduce experiments, and connect data collection, training, and evaluation. |
| Evolve — Agent as Self-Evolver | Use feedback to revise and retain reusable components, workflows, and research capabilities. |
- Robot policy interactions — Agent interactions with robot environments, including observations, decisions, tool calls, and feedback. Our research goal is to distill large-model teachers into compact policy agents that combine task accuracy with fast inference.
- Robotics R&D interactions — Coding-agent interactions from robotics development, integration, debugging, and testing. Our research goal is to use these traces for domain-specific post-training of open-weight models that can drive the RoboAgentLab harness.
The dataset cards describe the contents and structure of the current releases.
Our forthcoming paper, Agents that Act, Build, and Evolve: A Unified Framework and Platform for Agentic Robotics, connects these roles within a common framework. The RoboAgentLab platform and Awesome Agentic Robotics are available now.
Explore the platform repository and the datasets above to follow the project.