物理人工智能中的递归自我改进
Toward Recursive Self-Improvement in Physical AI
递归自改进（RSI）描述了不仅改进自身行为，还改进其改进过程的系统。近期数字智能体通过重写自身代码并使用可执行测试检查每个版本来测试这一想法。将此类机制引入物理世界需要通过昂贵试验从部分观察中学习，同时考虑不断变化的操作条件和潜在的不可逆后果。
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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来源: Hugging Face Daily Papers · huggingface.co