论文/arXiv 2610.10498·21 天前

EmbodiedRSI：通过假设引导的协同进化实现主动持续机器人学习

提出EmbodiedRSI，自主选择物理实验并将交互转化为代码和技能改进，在RoboCasa365、LIBERO-Pro及真实机器人验证。

AI 整理

• 硬件零部件
• 本体 SDK 与控制
• 数据与采集

原文链接：EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution（arXiv 2610.10498 · 发布日期：2026-10-07）

EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution

提出EmbodiedRSI，自主选择物理实验并将交互转化为代码和技能改进，在RoboCasa365、LIBERO-Pro及真实机器人验证。

核心要点

• 机器人基础模型在物体位置或任务指令变化时性能可能下降，后训练数据采集成本高。
• EmbodiedRSI以快慢双系统维护假设图，用信息价值选择实验并推动代码-技能协同进化。
• 慢系统构建层级记忆，并以奖励为依据选择有助于后续快系统改进的记忆。
• 在RoboCasa365总体成功率77.0%，LIBERO-Pro总体86.8%，并零样本迁移到真实机器人。

验证范围与局限：验证范围包括RoboCasa365、LIBERO-Pro基准，以及真实世界机器人上的多个挑战任务。

英文摘要 (Abstract)

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 substantial robot data, which can be costly to collect through methods such as teleoperation. Agentic harnesses can adapt around the model, but current self-evolving harnesses use robot trials inefficiently when deciding which code and skill changes to pursue. We introduce EmbodiedRSI, a self-evolving agentic harness that autonomously decides where to explore next and turns the resulting physical interaction into improved code and skills. EmbodiedRSI realizes this through a Fast-Slow Dual-System Architecture, in which competing code and skill hypotheses are maintained in a Hypothesis Graph. Value-of-Information Experiment Selection chooses physical experiments that can distinguish these hypotheses. Their outcomes guide Code-Skill Co-Evolution. The Slow System builds Hierarchical Memory, and Reward-Grounded Memory Learning selects effective memory according to their value for later Fast-System improvement. On RoboCasa365, EmbodiedRSI reaches 77.0% overall success and 71.3% on Composite-Unseen, compared with 40.1% for the best baseline. EmbodiedRSI also reaches 86.8% overall success on LIBERO-Pro. Beyond benchmark performance, EmbodiedRSI transfers zero-shot to real-world robot, achieving 71.3% overall success across multiple challenging tasks.

作者：Python Song, Zhixuan Liang, Fu Kelsey, Mengdi Wang, Junfeng Yang, Shilong Liu

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.10498：EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution访问 2026-10-21