Towards Recursive Self-Improvement in Physical AI
Toward Recursive Self-Improvement in Physical AI
Recursive Self-Improvement (RSI) describes systems that not only improve their own behavior but also improve their improvement processes. Recent digital agents have tested this idea by rewriting their own code and using executable tests to check each version. Introducing such mechanisms into the physical world requires learning from partial observations through expensive experiments while considering constantly changing operational conditions and potential irreversible consequences.
Published on Oct 21
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Abstract
Recursive self-improvement (RSI) describes a system that improves not only its behavior but also the process by which it improves. Recent digital agents test this idea by rewriting their own code and checking each version with executable tests. Bringing these mechanisms into the physical world requires learning from partial observations through costly trials, while accounting for changing operating conditions and potentially irreversible consequences. We position RSI within robot learning and trace pathways from improving robot capabilities to improving the processes that generate them. We examine how physical experience can strengthen these improvement mechanisms and when the resulting gains can support further self-improvement of the mechanisms themselves. Gains carry forward only when feedback addresses remaining learning needs, the receiving component can use the change, its value survives changes in tasks or hardware, and continued practice remains safe and affordable. Existing studies, concentrated in manipulation, support behavioral improvement, benefits to later learning, and procedure improvement. Some systems also change the improver's knowledge or tools, but the physical studies reviewed here have yet to isolate gains attributable to recursion. We synthesize these findings into evaluation principles and research directions for machines that, trial by trial, safely improve the way they improve.
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