Compounding Physical RSI proposed as robot policy self-evolution framework
What happened
Yixuan Yang and other authors proposed Compounding Physical RSI, a self-evolving policy framework aimed at turning robot execution failures into inheritable capabilities. The approach automatically assesses capability gaps, locates the task stage where failure occurs, then refines the policy using behavior-regularized offline reinforcement learning and constructs candidates in weight space to preserve existing skills. The authors state that the framework does not require costly online rollouts at each optimization step. The framework was evaluated on all 42 tasks of the RoboDojo-Sim benchmark, but the report does not provide specific performance results.
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- arXiv cs.ROCompounding Physical RSI: A Robot Policy Self-Evolution Framework That Turns Execution Failures into Inheritable Capabilities
The authors propose Compounding Physical RSI, a self-evolving policy framework that uses automated capability-gap assessment to locate the task stage where a failure occurs, refines policies with behavior-regularized offline reinforcement learning, and builds candidates in weight space to preserve existing skills.
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