PyRUA-REAL: Robot Exploration and Adaptive Learning through RSI in the Real World
Overview
This research introduces PyRUA-REAL, a framework enabling embodied recursive self-improvement (RSI) by having coding agents retain interaction experience as persistent memory and reusable executable skills. The authors evaluate the system across eight real-world tasks, achieving an average success rate of 91.1%, and on 42 RoboDojo benchmark tasks with an 84.4% average success rate. The work investigates how physical manipulation, execution feedback, and task specifications influence code refinement, robustness, and task transfer, ultimately providing empirical guidance for deploying reusable robot skills without online agent decision-making.
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Event evidence and corrections
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Reported quantity · Reported success rate: 91.1 percent · aggregate sensor hours · Differing source assertions
“skills influence robustness and task transfer. We further investigate the efficiency and capability of the resulting programs when deployed without involving an online agent for decision-making. Across eight real-world tasks, PyRUA-REAL achieves an average success rate of 91.1%. In complementary evaluation on 42 tasks from the RoboDojo benchmark, it achieves an average success rate of 84.4%. This study provides empi”
Exact source · revision 1Source owner not reported
Reported quantity · Reported success rate: 84.4 percent · aggregate sensor hours · Differing source assertions
“skills influence robustness and task transfer. We further investigate the efficiency and capability of the resulting programs when deployed without involving an online agent for decision-making. Across eight real-world tasks, PyRUA-REAL achieves an average success rate of 91.1%. In complementary evaluation on 42 tasks from the RoboDojo benchmark, it achieves an average success rate of 84.4%. This study provides empi”
Exact source · revision 1Source owner not reported
Reported quantity · number of tasks: 8 other · reported trials
“skills influence robustness and task transfer. We further investigate the efficiency and capability of the resulting programs when deployed without involving an online agent for decision-making. Across eight real-world tasks, PyRUA-REAL achieves an average success rate of 91.1%. In complementary evaluation on 42 tasks from the RoboDojo benchmark, it achieves an average success rate of 84.4%. This study provides empi”
Exact source · revision 1Source owner not reported
Reported quantity · number of benchmark tasks: 42 other · reported trials
“skills influence robustness and task transfer. We further investigate the efficiency and capability of the resulting programs when deployed without involving an online agent for decision-making. Across eight real-world tasks, PyRUA-REAL achieves an average success rate of 91.1%. In complementary evaluation on 42 tasks from the RoboDojo benchmark, it achieves an average success rate of 84.4%. This study provides empi”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalPyRUA-REAL: Robot Exploration and Adaptive Learning through RSI in the Real World
This research introduces PyRUA-REAL, a framework enabling embodied recursive self-improvement (RSI) by having coding agents retain interaction experience as persistent memory and reusable executable skills. The authors evaluate the system across eight real-world tasks, achieving an average success rate of 91.1%, and on 42 RoboDojo benchmark tasks with an 84.4% average success rate. The work investigates how physical manipulation, execution feedback, and task specifications influence code refinement, robustness, and task transfer, ultimately providing empirical guidance for deploying reusable robot skills without online agent decision-making.
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