Compounding Physical RSI: Turning Execution Failure into Inherited Capability
Overview
This research presents Compounding Physical RSI, a self-evolving policy framework that transforms execution failures into heritable capabilities. The method addresses three bottlenecks in physical environments: localizing failure stages, curating corrective experience without costly online rollouts, and safeguarding existing skills via weight-space candidates. Evaluated on the RoboDojo-Sim benchmark across 42 tasks, the proposed model (CSU-AI-07) ranks first with a mean Overall Score of 26.15 and Success Rate of 20.70%, outperforming the next-best entry by 5.92 points and 5.82 percentage points.
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Reported quantity · Overall Score: 26.15 other · Basis not reported
“or the next generation. Evaluated across all 42 tasks of the RoboDojo-Sim benchmark, CSU-AI-07 ranks first on the leaderboard with a three-seed mean Overall Score of 26.15 and Success Rate of 20.70%, outperforming the next-best entry by 5.92 points and 5.82 percentage points.”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 20.7 percent · Basis not reported
“or the next generation. Evaluated across all 42 tasks of the RoboDojo-Sim benchmark, CSU-AI-07 ranks first on the leaderboard with a three-seed mean Overall Score of 26.15 and Success Rate of 20.70%, outperforming the next-best entry by 5.92 points and 5.82 percentage points.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalCompounding Physical RSI: Turning Execution Failure into Inherited Capability
This research presents Compounding Physical RSI, a self-evolving policy framework that transforms execution failures into heritable capabilities. The method addresses three bottlenecks in physical environments: localizing failure stages, curating corrective experience without costly online rollouts, and safeguarding existing skills via weight-space candidates. Evaluated on the RoboDojo-Sim benchmark across 42 tasks, the proposed model (CSU-AI-07) ranks first with a mean Overall Score of 26.15 and Success Rate of 20.70%, outperforming the next-best entry by 5.92 points and 5.82 percentage points.
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