arXiv 2501.021162025-04-19cs.RO

人形机器人的 locomotion 与 manipulation：控制、规划与学习方面的当前进展与挑战

Humanoid Locomotion and Manipulation: Current Progress and Challenges in Control, Planning, and Learning

Georgia Institute of Technology（佐治亚理工学院）；The University of Southern California（南加州大学）；Technische Universität München（慕尼黑技术大学）；Google DeepMind（谷歌DeepMind）；The AI Institute（人工智能研究所）；The Institute for Human and Machine Cognition（人机认知研究所）；Duke University（杜克大学）；Kuwait University（科威特大学）；Stanford University（斯坦福大学）；CNRS-University of Montpellier（国家科学研究中心-蒙彼利埃大学）；LIRMM CNRS-AIST Joint Robotics Laboratory（联合机器人实验室）；Simon Fraser University（西蒙弗雷泽大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）；NVIDIA（英伟达）；Carnegie Mellon University（卡内基梅隆大学）；Harbin Institute of Technology（哈尔滨工业大学）

Zhaoyuan Gu, Junheng Li, Wenlan Shen, Wenhao Yu, Zhaoming Xie, Stephen McCrory, Xianyi Cheng, Abdulaziz Shamsah, Robert Griffin, C. Karen Liu, Abderrahmane Khed… 展开作者

Zhaoyuan Gu, Junheng Li, Wenlan Shen, Wenhao Yu, Zhaoming Xie, Stephen McCrory, Xianyi Cheng, Abdulaziz Shamsah, Robert Griffin, C. Karen Liu, Abderrahmane Kheddar, Xue Bin Peng, Yuke Zhu, Guanya Shi, Quan Nguyen, Gordon Cheng, Huijun Gao, Ye Zhao

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AI总结：

本综述系统梳理人形机器人移动与操作在控制、规划和学习方面的进展，对比基于模型与基于学习方法，并探讨基础模型与触觉感知的融合潜力。

摘要：

人形机器人具有执行各种人类水平技能的巨大潜力，包括在现实环境中统一进行移动与操作。在机器学习进展和现有基于模型的方法优势的推动下，这些能力已迅速发展，但往往彼此分离。本综述全面概述了人形机器人移动与操作（HLM）的最新进展，重点关注控制、规划和学习方法。我们首先回顾了过去三十年来作为人形机器人学支柱的基于模型的方法，讨论了接触规划、运动规划和全身控制，强调了模型保真度与计算效率之间的权衡。然后，重点转向考察新兴的基于学习的方法，着重于强化学习和模仿学习，这些方法增强了移动操作技能的鲁棒性和多样性。此外，我们评估了将基础模型与人形机器人实体集成的潜力，以促进通用人形智能体的发展。本综述还强调了触觉感知，特别是全身触觉反馈，作为处理接触丰富交互的关键模态的新兴作用。最后，我们从多个角度（如鲁棒性、计算效率、多样性和泛化能力）比较了基于模型和基于学习的范式的优缺点，并为现有挑战提出了可能的解决方案。

英文摘要：

Humanoid robots hold great potential to perform various human-level skills, involving unified locomotion and manipulation in real-world settings. Driven by advances in machine learning and the strength of existing model-based approaches, these capabilities have progressed rapidly, but often separately. This survey offers a comprehensive overview of the state-of-the-art in humanoid locomotion and manipulation (HLM), with a focus on control, planning, and learning methods. We first review the model-based methods that have been the backbone of humanoid robotics for the past three decades. We discuss contact planning, motion planning, and whole-body control, highlighting the trade-offs between model fidelity and computational efficiency. Then the focus is shifted to examine emerging learning-based methods, with an emphasis on reinforcement and imitation learning that enhance the robustness and versatility of loco-manipulation skills. Furthermore, we assess the potential of integrating foundation models with humanoid embodiments to enable the development of generalist humanoid agents. This survey also highlights the emerging role of tactile sensing, particularly whole-body tactile feedback, as a crucial modality for handling contact-rich interactions. Finally, we compare the strengths and limitations of model-based and learning-based paradigms from multiple perspectives, such as robustness, computational efficiency, versatility, and generalizability, and suggest potential solutions to existing challenges.

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。