Learning agile robotic policies requires large-scale demonstrations, but translating abundant human motion data to robots is bottlenecked by the embodiment gap and missing dynamic information. To bridge this gap, we propose Scalable Physics-Informed DExterous Retargeting (SPIDER), a physics-based retargeting framework to transform and augment kinematic-only human demonstrations into dynamically feasible robot trajectories at scale. Our key insight is that human demonstrations should provide global task structure and objective, while a sampling-based solver can be used to find the feasible solution given the physics constraints. As a general framework, SPIDER is an efficient physics-based retargeting method that can be applied to both humanoid whole-body loco-manipulation and dexterous manipulation across 9 humanoid/dexterous hand embodiments, 6 datasets and 3 simulators. By bypassing the need for policy optimization, it achieves state-of-the-art performance while being 10x faster than reinforcement learning (RL) baselines. Furthermore, SPIDER enables physics-based data augmentation, such as imposing external payloads, perturbations, and new contact patterns, to generate diverse data. We demonstrate that the retargeted motion can be executed on the robot directly open-loop or serve as feasible reference motion for efficient RL policy learning. Our pipeline enables large-scale generation of robot trajectories from human motion, and we release a dataset of 6,520 simulated demonstrations across four hands and 191 dataset-specific object identities to support future research.
Analyzing Key Objectives in Human-to-Robot Retargeting for Dexterous Manipulation
Department of Automation, Beijing National Research Center for Information Science and Technology, Tsinghua University, China（自动化系，信息科学与技术国家工程研究中心，清华大学）
DexFlow: A Unified Approach for Dexterous Hand Pose Retargeting and Interaction
Institute of Computer Science, Wuhan University, Hubei, China（武汉大学计算机科学学院，湖北，中国）；Department of Mechanical Engineering, Massachusetts Institute of Technology（麻省理工学院机械工程系）；Department of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程系）；Institute of Technological Sciences of Wuhan University, Wuhan, Hubei Province, China（武汉大学技术科学研究院，武汉，湖北省，中国）
DexTwist: Dexterous Hand Retargeting for Twist Motion via Mixed Reality-based Teleoperation
Autonomous Systems Lab, Institute of Computer Technology, TU Wien（自主系统实验室，计算机技术学院，维也纳技术大学）；Institute of Robotics and Mechatronics (DLR), German Aerospace Center（机器人与机电系统研究所（DLR），德国航空航天中心）
Tongji University（同济大学）；Shanghai Research Institute for Intelligent Autonomous Systems（上海自主智能无人系统研究院）；Purdue University（普渡大学）；Nanyang Technological University, Singapore（新加坡南洋理工大学）
ObjRetarget: An Object-Aware Motion Retargeting Framework with Anthropomorphic Arm Constraints and Polyhedral Hand Modeling
School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）人工智能学院）；School of Computing, University of Portsmouth（朴茨茅斯大学计算学院）
FunGrasp: Functional Grasping for Diverse Dexterous Hands
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；ETH Zürich（苏黎世联邦理工学院）；The Hong Kong University of Science and Technology（香港科技大学）
TypeTele: Releasing Dexterity in Teleoperation by Dexterous Manipulation Types
School of Computer Science and Engineering, Sun Yat-sen University, China（中山大学计算机科学与工程学院）；Stanford University, USA（斯坦福大学）；Imperial College London, UK（伦敦帝国理工学院）
DexTele: A Dual-Arm Dexterous Teleoperation System Based on Motion Retargeting and Adaptive Force Control
School of Artificial Intelligence, The Chinese University of Hong Kong, Shenzhen（人工智能学院，香港中文大学（深圳））；School of Computing, University of Portsmouth（计算学院，朴茨茅斯大学）
