Human2Humanoid: Physics-Aware Cross-Morphology Motion Retargeting for Humanoid Robots
Institute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China（人形机器人研究院，精密机械与精密仪器系，中国科学技术大学）
Retargeting human motion to humanoid robots is critical for teleoperation, imitation learning and human-robot interaction. However, it remains challenging because of substantial morphological discrepancies between humans and robots, including differences in skeletal topology, limb proportions and degrees of freedom, as well as the scarcity of paired motion data. This paper presents Human2Humanoid, an unsupervised motion retargeting framework that transfers human motions to humanoid robot behaviors with high fidelity. To bridge the domain gap under unpaired data, we adopt a CycleGAN-based architecture equipped with a skeleton-aware graph convolutional network to capture topology-dependent motion features. To address cross-domain scale mismatches, we introduce a morphology-invariant end-effector consistency loss that aligns normalized end-effector trajectories to preserve motion semantics across embodiments. To improve physical plausibility and reduce contact artifacts, we impose explicit physics-aware feasibility constraints to encourage reproduction of the contact patterns in the source motion. Experimental results show that the proposed method successfully retargets human motion to the Unitree G1 humanoid robot without paired data, and outperforms existing methods in both downstream controllability and physical feasibility.
Istituto Italiano di Tecnologia（意大利理工学院）；University of Genova（热那亚大学）；The University of Manchester（曼彻斯特大学）
Humanoid robot teleoperation allows humans to integrate their cognitive capabilities with the apparatus to perform tasks that need high strength, manoeuvrability and dexterity. This paper presents a framework for teleoperation of humanoid robots using a novel approach for motion retargeting through inverse kinematics over the robot model. The proposed method enhances scalability for retargeting, i.e., it allows teleoperating different robots by different human users with minimal changes to the proposed system. Our framework enables an intuitive and natural interaction between the human operato…
Unsupervised Neural Motion Retargeting for Humanoid Teleoperation
Graduate School of Informatics, Kyoto University（京都大学 informatics 研究生院）；Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST)（独立行政法人 产业技术综合研究所 人工智能研究中心）；Advanced Telecommunications Research Institute International (ATR)（国际电气通信基础技术研究所）；Graduate School of Science Technology, Nara Institute of Science and Technology（奈良科学技术研究所 研究生院）
From Language to Locomotion: Retargeting-free Humanoid Control via Motion Latent Guidance
University of Sydney（悉尼大学）；BAAI（北京人工智能研究院）；Harbin Institute of Technology（哈尔滨工业大学）；Hong Kong University of Science and Technology（香港科学与技术大学）；Shanghai Jiao Tong University（上海交通大学）；Peking University（北京大学）
Morphology-Consistent Humanoid Interaction through Robot-Centric Video Synthesis
Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Shenzhen University（深圳大学）；University of Cambridge（剑桥大学）
Georgia Institute of Technology（佐治亚理工学院）；Twentieth Century Fox（二十世纪福克斯公司）；Starfleet Academy（星际舰队学院）；Tyrell Inc.（泰伦公司）
This paper presents a multi-contact motion adaptation framework that enables teleoperation of high degree-of-freedom (DoF) robots, such as quadrupeds and humanoids, for loco-manipulation tasks in multi-contact settings. Our proposed algorithms optimize whole-body configurations and formulate the retargeting of multi-contact motions as sequential quadratic programming, which is robust and stable near the edges of feasibility constraints. Our framework allows real-time operation of the robot and reduces cognitive load for the operator because infeasible commands are automatically adapted into ph…
From Sign Language Generation to Humanoid Execution: Vision-Language Guided Retargeting with Collision Mitigation
Department of Systems and Control Engineering, Institute of Science Tokyo（东京科学大学系统与控制工程系）；RIKEN Guardian Robot Project（理化学研究所守护机器人项目）；ATR Hiroshi Ishiguro Laboratories（ATR石黑浩实验室）；RIKEN Center for Biosystems Dynamics Research（理化学研究所生物系统动力学研究中心）
School of Artificial Intelligence, Korea University（高丽大学人工智能学院）；Courant Institute of Mathematical Sciences, New York University（纽约大学柯朗数学科学研究所）；Technical University of Munich（慕尼黑工业大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
In this paper, we present self-supervised shared latent embedding (S3LE), a data-driven motion retargeting method that enables the generation of natural motions in humanoid robots from motion capture data or RGB videos. While it requires paired data consisting of human poses and their corresponding robot configurations, it significantly alleviates the necessity of time-consuming data-collection via novel paired data generating processes. Our self-supervised learning procedure consists of two steps: automatically generating paired data to bootstrap the motion retargeting, and learning a project…
Do You Have Freestyle? Expressive Humanoid Locomotion via Audio Control
BAAI（北京人工智能研究院）；University of Sydney（悉尼大学）；Harbin Institute of Technology（哈尔滨工业大学）；Hong Kong University of Science and Technology（香港科学与技术大学）；Shanghai Jiao Tong University（上海交通大学）；Peking University（北京大学）
Feasibility Retargeting for Multi-contact Teleoperation and Physical Interaction
Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Université de Lorraine（洛林大学）；University of Edinburgh（爱丁堡大学）；University College London（伦敦大学学院）；University of Sussex（萨塞克斯大学）
OTRetarget: Joint Robot and Object Motion Retargeting via Optimal Transport
Inria, Département d’Informatique de l’École Normale Supérieure, PSL Research University（法国国家信息与自动化研究所，巴黎高等师范学校计算机科学系，巴黎文理研究大学）；Stanford University（斯坦福大学）
A Whole-Body Motion Imitation Framework from Human Data for Full-Size Humanoid Robot
The State Key Laboratory of Industrial Control and Technology of Zhejiang University（浙江大学工业控制技术状态重点实验室）；Zhejiang Humanoid Robot Innovation Center（浙江人形机器人创新中心）；Department of Engineering Mechanics of Zhejiang University（浙江大学工程力学系）
BeyondRetarget: Learning Executable Humanoid Motions Directly from Monocular Video
Nanjing University（南京大学）；Jiangsu Mobile Information System Integration Co., Ltd.（江苏移动信息系统集成有限公司）；China Mobile Zijin (Jiangsu) Innovation Research Institute Co., Ltd.（中国移动紫金（江苏）创新研究院有限公司）
Fondazione Istituto Italiano di Tecnologia（意大利技术研究院基金会）；University of Manchester（曼彻斯特大学）
This article takes a step to provide humanoid robots with adaptive morphology abilities. We present a systematic approach for enabling robotic covers to morph their shape, with an overall size fitting the anthropometric dimensions of a humanoid robot. More precisely, we present a cover concept consisting of two main components: a skeleton, which is a repetition of a basic element called node, and a soft membrane, which encloses the cover and deforms with its motion. This article focuses on the cover skeleton and addresses the challenging problems of node design, system modeling, motor position…
RoboMirror: Understand Before You Imitate for Video to Humanoid Locomotion
Nanyang Technological University（南洋理工大学）；The Hong Kong University of Science and Technology（香港科技大学）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Xi’an Jiaotong University（西安交通大学）；Chinese Academy of Sciences（中国科学院）；Shanghai Jiao Tong University（上海交通大学）；University of Sydney（悉尼大学）；Peking University（北京大学）
