Playful Agentic Robot Learning
Authors: name: Junyi Zhang, name: Jiaxin Ge, name: Hanjun Yoo, name: Letian Fu, name: Zihan Yang, name: Yaowei Liu, name: Raj Saravanan, name: Shaofeng Yin, name: Justin Yu, name: Dantong Niu, name: Zirui Wang, name: Roei Herzig, name: Ken Goldberg, name: Yutong Bai, name: David M. Chan, name: Ion Stoica, name: Angjoo Kanazawa, name: Jiahui Lei, name: Haiwen Feng, name: Trevor Darrell
Organization: UC Berkeley

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 successful executions into a persistent code skill library. At test time, the agent reuses relevant skills from this frozen library to help solve new tasks. Experiments in LIBERO-PRO and MolmoSpaces show that play-learned skills improve held-out downstream tasks over no-play and random-play baselines, with 20.6 and 17.0 percentage-point gains over CaP-Agent0 on LIBERO-PRO and MolmoSpaces, respectively. Moreover, the learned skills can be plugged into other inference-time Code-as-Policy agents by simply retrieving them into the context, improving RoboSuite and real-world transfer by 8.9 and 8.8 points, respectively, without finetuning the underlying model.