arXiv:2606.19419v1 Announce Type: cross Abstract: 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
The rapid advancement in large language models and reinforcement learning is enabling more sophisticated agentic systems capable of complex problem-solving and self-improvement.
This research outlines a methodology for robots to acquire reusable skills autonomously through self-directed play, significantly reducing the need for explicit programming and accelerating their utility.
Robot learning shifts from primarily task-driven instruction to a more autonomous, exploratory, and continuous skill acquisition process, making robots more adaptable and versatile.
- · Robotics companies
- · AI software developers
- · Automation sector
- · Traditional industrial programmers
- · Companies reliant on single-task automation
Embodied AI agents become significantly more capable of learning and adapting to unstructured environments.
This foundational ability leads to a proliferation of more versatile and commercially viable humanoid robots and other agentic systems.
Autonomous robot workforces, capable of continuous on-the-job skill acquisition, begin to displace human labour in a wider range of physical and cognitive tasks.
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Read at arXiv cs.AI