Flip Anything with Agentic Robotics Self-Learning
Overview
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.
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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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- arXiv Robotics — research abstractsSignalFlip 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.
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