发表机构
Italian Institute of Technology（意大利技术研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对大型四足机器人执行器动力损失的容错运动问题，提出带潜在对齐损失的非对称Actor-Critic深度强化学习方法，通过自适应步态频率实现容错，经仿真与真实实验验证有效。
AI 中文摘要
硬件故障要求四足机器人快速调整协调机制与步态时序以维持稳定性和移动能力，这对大型四足机器人尤为具有挑战性——其更大的质量和更严格的驱动限制，使得小型平台常用的激进高频补偿策略可行性降低。本研究提出一种用于执行器动力损失下容错运动的深度强化学习方法，该方法采用非对称Actor-Critic架构：Critic在训练期间可获取特权信息，Actor则需从本体感受观测中学习重构对应的潜在表示。我们引入潜在对齐损失以促使Actor与Critic表示保持一致；此外，我们在动作空间中增加可学习的步态频率参数，使机器人能响应地形变化与执行器退化实现自适应步态时序，无需预定义故障腿策略。该方法在不平地形的高保真仿真及68kg四足机器人的平地真实实验中得到验证。
英文摘要
Hardware failures require legged robots to rapidly reorganize coordination and gait timing to maintain stability and mobility. This is particularly challenging for larger quadrupeds, where increased mass and tighter actuation limits reduce the feasibility of aggressive, high-frequency compensation strategies often observed on smaller platforms. In this work, we propose a deep reinforcement learning approach for fault-tolerant locomotion under actuator power loss. The method employs an asymmetric actor-critic architecture in which the critic has access to privileged information during training, while the actor learns to reconstruct a corresponding latent representation from proprioceptive observations. We introduce a latent-alignment loss that encourages consistency between actor and critic representations. Additionally, we augment the action space with a learnable gait frequency parameter, enabling adaptive gait timing in response to terrain variations and actuator degradation without predefined faulty-leg strategies. The approach is validated in high-fidelity simulation on uneven terrain and real-world experiments on flat ground using a 68 kg quadruped robot.
CommentsAccepted at the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
