arXiv AI
Who Improves the Robot? Toward Physical Recursive Self-Improvement
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The paper discusses recursive self‑improvement (RSI) in physical AI, where a system not only enhances its behavior but also the methods it uses to improve. It reviews how robot learning can evolve both capabilities and improvement processes, highlighting that gains persist only when feedback addresses remaining learning needs, the component can apply the change, the value survives task or hardware shifts, and practice remains safe and affordable. Current physical studies mainly show behavioral and procedural gains, but have not yet isolated recursive benefits, leading the authors to propose evaluation principles and future research directions for safely trial‑by‑trial self‑improvement.
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Hugging Face Trending Papers
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EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution
Robot foundation models provide strong visuomotor control, yet their performance can degrade when object positions or task instructions change. Further improvements often require post-training on subs...
arXiv AI
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