Flip Anything with Agentic Robotics Self-Learning
Flip Anything with Agentic Robotics Self-Learning
This paper presents an agentic robotics approach where a coding agent autonomously develops and refines object-flipping strategies through iterative learning in physics simulations. The method progresses from basic skills like handover and pushing to robust grasping and thin-object manipulation, improving task success from 24.7% to 95.0%. The resulting graph-based code integrates high-level strategy selection with reactive control, enabling multi-stage flipping on physical robots.
The authors demonstrate a coding agent that autonomously learns object-flipping strategies in physics simulations, achieving a task success rate of 95.0% on diverse geometries after progressing from basic handover to thin-object manipulation with recovery.
Source: arXiv Robotics — research abstracts · Read original article ↗
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tool use, coding a bimanual robot with grippers to flip diverse objects is a significant challenge. In this paper, we show that a coding agent can autonomously develop and refine object-flipping strategies through iterative learning in physics simulations across diverse object geometries. Progressive skill acquisition, from basic handover to toppling and pushing, robust grasping and sensing, and thin-object manipula
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tion with recovery, improves task success from 24.7% to 95.0%. The resulting graph-based code integrates high-level strategy selection with reactive control, enabling multi-stage flipping on physical robots.
Open source S4
Source：arXiv Robotics — research abstracts · arxiv.org