Playful Agentic Robot Learning
Current agentic robot systems can write executable Code-as-Policy programs, observe feedback, and revise behavior across multiple attempts, but they remain largely task-driven: reusable skills are acquired only after explicit instructions. We study Playful Agentic Robot Learning, where an embodied coding agent uses self-directed play as a continual skill-learning stage before downstream tasks arrive. We introduce RATs, Robotics Agent Teams designed for play-time skill acquisition. During play, RATs proposes novel yet learnable exploratory tasks, plans and executes robot-code policies, verifies intermediate progress, diagnoses failures, retries with dense, step-level feedback, and distills...
| Authors | Junyi Zhang, Jiaxin Ge, Hanjun Yoo et al. |
|---|---|
| Primary category | cs.RO |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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