PyRUA-REAL: Robot Exploration and Adaptive Learning through RSI in the Real World

WANG QIANG, Jianxin Bi, Shulong Jiang, RuiyangSi, Wenbo Huang, Kaiqi Chen, Jiaxin Li, Liyun Li, Xuanyi Li, Daquan Zhou

PyRUA-REAL: Robot Exploration and Adaptive Learning through RSI in the Real World: 11 upvotes on Hugging Face Daily Papers, #19 of 38 papers on 2026-11-05. Day-by-day upvote history.

Upvotes over time

It had 9 upvotes by its first day, 10 upvotes by the next day, 11 upvotes by day 3, 11 upvotes by day 7, and has 11 now. It ranked #107 of 207 papers in its week and #160 of 340 in its month.

Coding agents powered by models such as GPT-6-Astra connect spatial reasoning with executable robot behavior, raising the question of how such agents can produce lasting improvements in robot capabilities from physical interaction. In this work, we study embodied recursive self-improvement (RSI) through real-world exploration, code refinement, and deployment using PyRUA-REAL, a framework in which coding agents retain interaction experience as persistent memory and reusable executable skills that inform subsequent exploration and improvement. Our empirical study places physical manipulation at the center of the improvement process, examining how agents collaborate, what kinds of execution feedback support effective code refinement, and how task specifications and previously acquired 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 empirical guidance for turning physical exploration into reusable robot skills that support continued improvement and robust, efficient deployment.

Paper page on Hugging Face · arXiv

Data: hysts-bot-data/daily-papers-stats and the Daily Papers API. Open data: tardellirs/paper-pulse-data. Sister project: Model Pulse, the download history of every model on the Hub.