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| title: RoboAgentLab | |
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| <img src="https://huggingface.co/spaces/RoboAgentLab/README/resolve/main/avatar.png" width="100" alt="RoboAgentLab robot arm and code brackets"> | |
| <h1>RoboAgentLab</h1> | |
| <p><strong>Agents that Act, Build, and Evolve.</strong></p> | |
| <p>An open research platform for agentic robotics.</p> | |
| <p> | |
| <a href="https://roboagentlab.github.io/">Website</a> · | |
| <a href="https://roboagentlab.github.io/docs/quickstart.html">Get started</a> · | |
| <a href="https://github.com/RoboAgentLab/RoboAgentLab">Platform code</a> · | |
| <a href="https://github.com/RoboAgentLab/awesome-agentic-robotics">Awesome list</a> · | |
| <a href="https://huggingface.co/RoboAgentLab/datasets">Datasets</a> | |
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| ## Robotics as software engineering | |
| RoboAgentLab brings robot task execution and experimental development into a shared coding-agent workspace. Built on [pi](https://github.com/earendil-works/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. | |
| ## Three complementary roles | |
| | 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. | | |
| ## Two streams of research data | |
| - **[Robot policy interactions](https://huggingface.co/datasets/RoboAgentLab/pi-embodied-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](https://huggingface.co/datasets/RoboAgentLab/pi-embodied-rd-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. | |
| ## Research and open resources | |
| 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](https://github.com/RoboAgentLab/RoboAgentLab) and [Awesome Agentic Robotics](https://github.com/RoboAgentLab/awesome-agentic-robotics) are available now. | |
| Explore the [platform repository](https://github.com/RoboAgentLab/RoboAgentLab) and the datasets above to follow the project. | |