发表机构
School of Control Science and Engineering, Shandong University; Shandong Key Laboratory of Humanoid Robotics（山东大学控制科学与工程学院; 山东省类人机器人重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出DAMP强化学习框架，利用循环神经网络隐式推断任务相关潜在信息，实现无感知信息下人形机器人在复杂地形的鲁棒运动，可完成仿真到真实环境的迁移，验证了其鲁棒性与泛化能力。
AI 中文摘要
人形机器人具备在复杂地形移动的结构能力，但在不依赖感知信息的情况下实现稳定移动仍具挑战性，尤其在复杂环境中。本文提出DAMP，一种强化学习框架，旨在在无感知信息可用的假设下，实现人形机器人在复杂地形上的鲁棒且自然的运动。该框架利用循环神经网络捕捉时间依赖关系，隐式推断特权信息及其他与任务相关的潜在信息；通过将学习到的表征与任务目标对齐，实现鲁棒且与目标一致的策略学习。该端到端框架可实现从仿真到真实环境的迁移学习，验证了所提方法的鲁棒性与泛化能力，真实环境演示视频可通过指定链接获取。
英文摘要
Humanoid robots possess the structural capability to traverse complex terrains. However, achieving stable t raversal without relying on perceived information remains challenging, particularly in complex environments. This paper introduces DAMP, a reinforcement learning framework aimed at achieving robust and naturalistic humanoid locomotion over challenging terrains, with the assumption that no perceived information is available. The framework leverages recurrent neural networks to capture temporal dependencies and implicitly infer privileged and other task-relevant latent information. By aligning the learned representations with the task objective, the method enables robust and goal-consistent policy learning. This end-to-end framework achieves transfer learning from simulation to real-world environments, demonstrating the proposed method's robustness and generalization capabilities. The video of the real-world demonstration can be found at the following link: https://youtu.be/AkI7TZB2DDM.
