English(EN) Joint-Health-Aware Reinforcement Learning for Robust Robot Control Under Actuator Degradation

新框架改善执行器退化下的机器人控制

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-02 05:00

研究人员开发了一种新颖的强化学习框架，旨在提高机器人面对执行器退化时的控制鲁棒性。该系统名为HealthNet，可从近期观测和动作中推断出各个关节的健康状态，从而使强化学习策略能够动态地重新分配控制力。该框架将退化建模为执行动作的连续、乘性衰减，与依赖离散故障标签或广泛域随机化的方法相比，能够实现更细致的适应。在各种机器人平台和操作任务上的评估表明，在严重退化场景下，移动和操作成功率得到了显著提高。 AI

影响通过在执行器退化下实现自适应控制，提高了机器人在非结构化环境中的可靠性。

排序理由详细介绍机器人控制新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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arXiv cs.CV TIER_1 English(EN) · Haifa Zhang, Yijing Wang, Zhiqiang Zuo · 2026-11-02 05:00

Joint-Health-Aware Reinforcement Learning for Robust Robot Control Under Actuator Degradation

arXiv:2610.41348v1 Announce Type: cross Abstract: Robots deployed in unstructured environments are susceptible to actuator-authority degradation, which reduces the effective control authority of individual actuators and can cause reinforcement learning policies trained under nomi…

本文如何被排名

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Joint-Health-Aware Reinforcement Learning for Robust Robot Control Under Actuator Degradation

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