PyRUA-REAL
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- PyRUA-REAL Turns Robot Exploration into Reusable Skills
A paper introduces PyRUA-REAL, a framework for embodied recursive self-improvement in which coding agents retain physical interaction experiences as persistent memory and reusable executable skills. The authors report an average success rate of 91.1% across eight real-world tasks and 84.4% across 42 tasks from the RoboDojo benchmark, including deployment without an online agent for decision-making. The paper also examines agent collaboration, execution feedback for code refinement, task specifications, robustness, and skill transfer.
Hugging Face · Papers · 🔥 0
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