arXiv 2602.055962026-02cs.RO

TOLEBI：通过在线状态估计和易错奖励学习容错双足运动

TOLEBI: Learning Fault-Tolerant Bipedal Locomotion via Online Status Estimation and Fallibility Rewards

Department of Intelligence and Information, Graduate School of Convergence Science and Technology, Seoul National University（智能信息系，融合科学与技术研究生院，首尔国立大学）；Artificial Intelligence Institute (AIIS), Seoul National University（人工智能研究院（AIIS），首尔国立大学）；Advanced Institutes of Convergence Technology (AICT), Suwon（融合技术高级研究院（AICT），Suwon）

Hokyun Lee, Woo-Jeong Baek, Junhyeok Cha, Jaeheung Park

备注：: Accepted for Publication at IEEE International Conference on Robotics and Automation (ICRA) 2026

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AI总结：

TOLEBI通过在线状态估计和易错奖励，首次提出基于学习的双足运动容错框架，提升机器人在现实环境中的运动稳定性与容错能力。

摘要：

随着学习算法在机器人应用中的日益普及，强化学习在双足运动中的研究已成为人形机器人领域的核心课题。尽管最近发表的成果在运动任务中实现了较高的成功率，但对能够处理运动过程中可能出现的硬件故障的方法开发却关注不足。然而，在现实环境中，环境扰动或突发的硬件故障可能导致严重后果。为了解决这些问题，本文提出了TOLEBI（一种容错学习框架用于双足运动），该框架在机器人运行过程中处理故障。具体来说，在模拟中注入关节锁死、电源失效和外部干扰，以学习容错的运动策略。除了通过仿真到现实转移将学习的策略转移到现实机器人外，还集成了一个在线关节状态模块。该模块能够通过实际观察在现实条件下对关节状态进行分类。在人形机器人TOCABI上进行的现实和仿真验证实验突显了所提出方法的适用性。据我们所知，本文提供了首个基于学习的双足运动容错框架，从而促进了该领域高效学习方法的发展。

英文摘要：

With the growing employment of learning algorithms in robotic applications, research on reinforcement learning for bipedal locomotion has become a central topic for humanoid robotics. While recently published contributions achieve high success rates in locomotion tasks, scarce attention has been devoted to the development of methods that enable to handle hardware faults that may occur during the locomotion process. However, in real-world settings, environmental disturbances or sudden occurrences of hardware faults might yield severe consequences. To address these issues, this paper presents TOLEBI (A faulT-tOlerant Learning framEwork for Bipedal locomotIon) that handles faults on the robot during operation. Specifically, joint locking, power loss and external disturbances are injected in simulation to learn fault-tolerant locomotion strategies. In addition to transferring the learned policy to the real robot via sim-to-real transfer, an online joint status module incorporated. This module enables to classify joint conditions by referring to the actual observations at runtime under real-world conditions. The validation experiments conducted both in real-world and simulation with the humanoid robot TOCABI highlight the applicability of the proposed approach. To our knowledge, this manuscript provides the first learning-based fault-tolerant framework for bipedal locomotion, thereby fostering the development of efficient learning methods in this field.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。