机器人如何实现物理递归自我改进？Physical RSI 的 L0-L5 框架分析
Who Improves the Robot? Toward Physical Recursive Self-Improvement
研究提出 Physical Recursive Self-Improvement（Physical RSI）概念，即物理经验能持续更新机器人能力及其改进流程，并用 L0-L5 框架区分能力级与元级更新、任务内与跨任务收益，仅将 L5 留给独立验证的递归增益。
Authors:Hai Zhou, Fan Yang, Zeyi Chen, Lejun Zhang, Chenyang Fu, Jianping Li, Jiayi Zhu, Xianyang Liu, Yingxuan Yang, Lili Meng, Lu Wang, Xinyang Han, Jiankai Sun, Weirui Ye, Guanchu Wang, Wanhua Li, Ming Zhou, Boyi Li, Ming Jin, Kun Shao, Muhao Chen, Guang Chen, Lei Ma, Joey Tianyi Zhou, Costas Spanos, Xia Hu, Mac Schwager, Weinan Zhang, Qian Feng, Shangding Gu
Abstract:Robots are increasingly autonomous in task execution, yet their improvement remains largely human-driven. This study investigates Physical Recursive Self-Improvement (Physical RSI), in which physical experience guides lasting updates to both a robot's capabilities and its improvement procedure, making subsequent improvement more effective across tasks. Physical RSI faces challenges from noisy observations, continuous dynamics, costly trials, and irreversible consequences. We present an L0-L5 framework that differentiates capability-level from meta-level updates and within-task from across-task gains, reserving L5 for independently verified recursive gain. We study existing methods around harness adaptation and model training, alongside safety and control mechanisms that govern execution and successive updates. Our analysis reveals that, despite substantial progress in self-improvement, the surveyed literature does not yet demonstrate Physical RSI as defined by our framework. We identify research priorities in reliable feedback, causal attribution, safety preservation, and benchmarking, outlining a path toward robots that autonomously improve how they learn while ensuring safety. The GitHub repository for Physical RSI is available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO) |
| Cite as: | arXiv:2610.18842 [cs.AI] |
| (or arXiv:2610.18842v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.18842 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lejun Zhang [view email]
[v1]
Tue, 13 Oct 2026 17:57:04 UTC (21,075 KB)
来源：arXiv：cs.AI · arxiv.org