The paper discusses recursive self‑improvement (RSI) in physical AI, where a system not only enhances its behavior but also the methods it uses to improve. It reviews how robot learning can evolve both capabilities and improvement processes, highlighting that gains persist only when feedback addresses remaining learning needs, the component can apply the change, the value survives task or hardware shifts, and practice remains safe and affordable. Current physical studies mainly show behavioral and procedural gains, but have not yet isolated recursive benefits, leading the authors to propose evaluation principles and future research directions for safely trial‑by‑trial self‑improvement.
By Meng Luo, Zechen Bai, Shanqing Xu, Yanlin Li, Kaixin Li, Mengchen Qian, Weifeng Lu, Ran Zhang, Yingtian Zou, Ziyang Luo, Wei Chen, Hongzhan Lin
arXiv:2610.35604v1 Announce Type: cross
Abstract: Generalist robot policies and language-model agents provide complementary capabilities, but completing a task also requires adapting their use to the...
By Heyang Yu, Haomin Li, Sihang Li, Jixin Yan, Shiyun Peng, Wanqi Zhong, Tianyu Xin, Yuan Yao, Xiaoyu Tian, Yiming Li
arXiv:2606. 19980v1 Announce Type: new Abstract: Achieving dexterous robotic manipulation in the real world heavily relies on human supervision and algorithm engineering, which becomes a central bottleneck in the pursuit of general physical intelligence.
By Wenli Xiao, Jia Xie, Tonghe Zhang, Haotian Lin, Letian "Max" Fu, Haoru Xue, Jalen Lu, Yi Yang, Cunxi Dai, Zi Wang, Jimmy Wu, Guanzhi Wang, S. Shankar Sastry, Ken Goldberg, Linxi "Jim" Fan, Yuke Zhu, Guanya Shi
RoboRSI is a robot self‑improvement system that uses Top‑Down Skill Refinement to decompose tasks into compound, atomic, and base skills with clear responsibilities and input‑output contracts. The system coordinates a Manager, Planner, Engineer, and Reviewer to plan, execute, diagnose, and validate skill revisions, allowing humans to guide objectives and corrections. On a mobile manipulator, RoboRSI successfully learns multi‑object household cleanup over 104 rounds and outperforms the strongest baseline by 2.7 to 11.0 percentage points on several simulation benchmarks.
By Zimo Wen, Yijin Chen, Yuxuan Cao, Wendi Chen, Yanwen Zou, Wenye Yu, Fuhang Kuang, Han Xue, Jun Lv, Chuan Wen, Cewu Lu
Robot foundation models provide strong visuomotor control, yet their performance can degrade when object positions or task instructions change. Further improvements often require post-training on subs...
arXiv:2609.38178v1 Announce Type: cross
Abstract: Robots deployed in the physical world must be able to improve beyond their initial training as they encounter new situations and failures. For this i...
By Zihang Rui, Renhao Wang, Haoxu Huang, Yang Gao
arXiv:2610.20474v1 Announce Type: new
Abstract: Pretrained vision-language-action models (VLAs) often fail midway through unseen long-horizon tasks. Recursive self-improvement (RSI) offers a remedy,...
By Peidong Wang, Ying Chang, Zhiming Ma, Xiaocui Yang, Shi Feng, Daqi Liu, Yuzhe Yang, Xu Cao, Chuang Wang, Jian Su, Daling Wang
The paper introduces PARTS, a real‑world subtask reinforcement learning framework that fine‑tunes a pretrained robot policy by focusing on critical bottleneck subtasks while keeping the base policy frozen. It uses agent‑generated selectors and success verifiers to provide local rewards, enabling learning even when full‑task successes are rare. Experiments on bimanual YAM and single‑arm Franka robots show that PARTS raises complete‑task success from 32% to 61% and from 50% to 95%, respectively, with only tens of minutes of real‑world RL rollouts and minimal human intervention.
By Sichang Su, Benjamin Yang, Zhiyun Deng, Boyuan Liang, Yip Fun Yeung, Zelin Wang, Lingfeng Sun
Reconstruct, Practice, Go Real (RPG) is a framework that enables autonomous improvement of robot execution systems without updating model weights. RPG identifies manipulation capabilities from offline data, creates simulation practice tasks, and uses feedback to diagnose failures, develop new symbolic skills, refine existing ones, and revise the system prompt. Across 22 manipulation tasks, RPG raises task success from 28.6% after the first practice round to 95.0% after 15 rounds, outperforming baselines and achieving perfect success on 30 physical trials after calibration.
By Yen-Jen Wang, Haozhe Jiang, Shuying Deng, Haoru Xue, Weirui Ye, Rocky Duan, Nika Haghtalab, S. Shankar Sastry, Pieter Abbeel, Haozhi Qi
arXiv:2610.08761v1 Announce Type: new
Abstract: Self-improving policies continually expose new failure patterns, changing what their judges must be able to verify. However, current fixed judges const...
By Zewei Zhou, Rachel Luo, Yulong Cao, Chaowei Xiao, Chensheng Peng, Boyi Li, Thomas Tian, Zheng Lian, Yan Wang, Jiaqi Ma, Boris Ivanovic, Marco Pavone, Wenhao Ding
arXiv:2609.40306v1 Announce Type: cross
Abstract: Pretrained robot policies provide useful action priors, but long-horizon manipulation still requires coordination between semantic reasoning and phys...
By Haoyuan Deng, Jiebin Liu, Tengxiao Zhang, Langning Yan, Hongye Cao, Ziwei Wang
arXiv:2607. 07663v1 Announce Type: new Abstract: AI systems increasingly participate in their own improvement: revising their outputs, adapting their own harnesses during deployment, training on data they generate, and, increasingly, conducting AI research itself.
By Mingguang Chen, Licheng Wang, Bo Qu