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基于形态的具身智能研究: 历史回顾与前沿进展

刘华平 郭迪 孙富春 张新钰

刘华平, 郭迪, 孙富春, 张新钰. 基于形态的具身智能研究: 历史回顾与前沿进展. 自动化学报, 2023, 49(6): 1131−1154 doi: 10.16383/j.aas.c220564
引用本文: 刘华平, 郭迪, 孙富春, 张新钰. 基于形态的具身智能研究: 历史回顾与前沿进展. 自动化学报, 2023, 49(6): 1131−1154 doi: 10.16383/j.aas.c220564
Liu Hua-Ping, Guo Di, Sun Fu-Chun, Zhang Xin-Yu. Morphology-based embodied intelligence: Historical retrospect and research progress. Acta Automatica Sinica, 2023, 49(6): 1131−1154 doi: 10.16383/j.aas.c220564
Citation: Liu Hua-Ping, Guo Di, Sun Fu-Chun, Zhang Xin-Yu. Morphology-based embodied intelligence: Historical retrospect and research progress. Acta Automatica Sinica, 2023, 49(6): 1131−1154 doi: 10.16383/j.aas.c220564

基于形态的具身智能研究: 历史回顾与前沿进展

doi: 10.16383/j.aas.c220564
基金项目: 国家自然科学基金(62025304, 62273054)资助
详细信息
    作者简介:

    刘华平:清华大学计算机科学与技术系教授. 2004年获得清华大学博士学位. 国家杰出青年科学基金获得者. 现担任Internationa Journal of Robotics Research的Senior editor. 主要研究方向为具身感知与学习. 本文通信作者. E-mail: hpliu@tsinghua.edu.cn

    郭迪:北京邮电大学人工智能学院教授. 2017年获得清华大学博士学位. 主要研究方向为具身交互感知. E-mail: guodi.gd@gmail.com

    孙富春:清华大学计算机科学与技术系教授. 1997年获得清华大学博士学位. 国家杰出青年科学基金获得者. 中国人工智能学会副理事长, IEEE Fellow. 主要研究方向为行为智能. E-mail: fcsun@tsinghua.edu.cn

    张新钰:清华大学车辆与运载学院清华猛狮无人驾驶车队负责人. 2001年获得清华大学学士学位. 主要研究方向为具身形态智能. E-mail: xyzhang@tsinghua.edu.cn

Morphology-based Embodied Intelligence: Historical Retrospect and Research Progress

Funds: Supported by National Natural Science Foundation of China (62025304, 62273054)
More Information
    Author Bio:

    LIU Hua-Ping Professor in the Department of Computer Science and Technology, Tsinghua University. He received his Ph.D. degree from Tsinghua University in 2004. He was a recipient of the National Science Fund for Distinguished Young Scholars. Currently, he is a senior editor of International Journal of Robotics Research. His research interest covers embodied perception and learning. Corresponding author of this paper

    GUO Di Professor at the School of Artificial Intelligence, Beijing University of Posts and Telecommunications. She received her Ph.D. degree from Tsinghua University in 2017. Her main research interest is embodied interactive perception

    SUN Fu-Chun Professor in the Department of Computer Science and Technology, Tsinghua University. He received his Ph.D. degree from Tsinghua University in 1997. He was a recipient of the National Science Fund for Distinguished Young Scholars. He serves as the vice director of Chinese Association for Artificial Intelligence. He is an IEEE Fellow. His main research interest is behaviour intelligence

    ZHANG Xin-Yu Head of the Mengshi Intelligent Vehicle Team, School of Vehicle and Mobility, Tsinghua University. He received his bachelor degree from Tsinghua University in 2001. His main research interest is embodied morphology intelligence

  • 摘要: 具身智能强调智能受脑、身体与环境协同影响, 更侧重关注智能体与环境的“交互”. 因此, 在具身智能的研究中, 智能体的物理形态与感知、学习、控制的关系起到至关重要的作用. 当前, 具身智能综合吸收了机构学领域关于形态、结构, 机器学习领域关于感知、学习, 以及机器人领域关于行为、控制等的相关研究成果, 形成了相对完整、独立并仍在蓬勃发展的学科分支. 但是, 目前尚无文献完整地梳理基于形态的具身智能研究进展. 本文从这个角度出发, 重点围绕基于形态计算的行为生成、基于学习的形态控制, 以及基于学习的形态优化这三方面总结重要的研究进展, 凝炼相关的科学问题, 并总结未来的发展方向, 可为具身智能的研究提供参考.
  • 图  1  基于形态的具身智能的体系架构

    Fig.  1  The architecture of morphology-based embodied intelligence

    图  2  瓦特发明的蒸汽机离心调速器与现代自动控制结构的对比

    Fig.  2  The comparison between the steamer centrifugal governor invented by Watt and the structure of modern automatic control

    图  3  (a) 被动机器人原理样机[26], 经许可转载自文献[26], ©AAAS, 2005; (b) 三类机器人平台[26]: A: 康奈尔机器人; B: 德尔夫特机器人; C: MIT机器人, 经许可转载自文献[26], ©AAAS, 2005

    Fig.  3  (a) Passive robot prototypes[26], reproduced with permission from reference [26], ©AAAS, 2005; (b) three robotic platforms[26]: A: Cornel robot; B: Delft robot; C: MIT robot, reproduced with permission from reference [26], ©AAAS, 2005

    图  4  具身形态计算的储备池计算模型

    Fig.  4  The reservoir computing models for embodied morphology computing

    图  5  24 自由度张力平衡型 (Tensegrity) 机器人, 经许可转载自文献[35], ©Elsevier, 2006

    Fig.  5  A 24-DoF tensegrity robot, reproduced with permission from reference [35], ©Elsevier, 2006

    图  6  典型的储备池计算具身形态计算装置

    Fig.  6  Typical embodied morphology computing devices of reservoir computing

    图  7  具身形态计算的典型信息论分析方法结构[57]

    Fig.  7  Typical structure for embodied morphology computing based on theory of information[57]

    图  8  基于图神经网络的形态控制学习结构

    Fig.  8  Morphology control learning structure based on GNN

    图  9  典型形态的图结构[72]

    Fig.  9  Typical graph structure of morphology[72]

    图  10  用于图神经网络描述的模块化结构设计[75]

    Fig.  10  Modular structure design for GNN description[75]

    图  11  典型的Transformer结构

    Fig.  11  Typical structure of Transformer

    图  12  物理可实现的进化机器人, 经许可转载自文献[84], ©IEEE, 2000

    Fig.  12  Physically realizable development robot, reproduced with permission from reference [84], ©IEEE, 2000

    图  13  基于CPPN 的形态与控制协同进化.每行对应一组进化结果[89]

    Fig.  13  Collaborative evolving of morphology and control based on CPPN. One row indicates one group of evolving result[89]

    图  14  基于强化学习的形态−控制协同优化, 经许可转载自文献[97], ©IEEE, 2019

    Fig.  14  Morphology-control collaborative optimization based on reinforcement learning, reproduced with permission from reference [97], ©IEEE, 2019

    图  15  机械手形态设计优化

    Fig.  15  The optimization for robotic hand morphology

    图  16  迁移到物理系统上的形态进化举例

    Fig.  16  Examples of Sim2Real morphology evolving

    图  17  直接对物理机器人系统进行形态进化发育的机械臂操作装置[116]

    Fig.  17  Robotic arm devices which directly conduct morphology evolving with physical robotic system[116]

    图  18  四腿机器人物理进化系统, 经许可转载自文献[118], ©IEEE, 2019

    Fig.  18  Quadruped robot evolving system, reproduced with permission from reference [118], ©IEEE, 2019

    图  19  基于软体机器人的具身形态计算

    Fig.  19  Embodied morphology computing based on soft robot

    图  20  软体机器人形态进化(不同颜色对应不同的材料特性)

    Fig.  20  Morphology evolving of soft robot (different colors correspond to different material characteristics)

    图  21  体素软体机器人的形态优化

    Fig.  21  The morphology optimization of voxel-based soft robots

    图  22  基于形态的具身智能的主要研究途径. 其中方框代表这一领域面临的问题, 椭圆框代表主要解决的途径

    Fig.  22  Approaches for morphology-based embodied intelligence, among which squares are challenges and ellipses are approaches to solve the challenges

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  • 收稿日期:  2022-07-09
  • 录用日期:  2023-01-16
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