Dexterous hand teleoperation requires motion re-targeting methods that simultaneously achieve high-frequency real-time performance and enforcement of heterogeneous kinematic and safety constraints. Existing nonlinear optimization-based approaches often incur prohibitive computational cost, limiting their applicability to kilohertz-level control, while learning-based methods typically lack formal safety guarantees. This paper proposes a scalable motion retargeting framework that reformulates the nonlinear retargeting problem into a convex quadratic program in joint differential space. Heterogeneous constraints, including kinematic limits and collision avoidance, are incorporated through systematic linearization, resulting in improved computational efficiency and numerical stability. Control barrier functions are further integrated to provide formal safety guarantees during the retargeting process. The proposed framework is validated through simulations and hardware experiments on the Wuji Hand platform, outperforming state-of-the-art methods such as Dex-Retargeting and GeoRT. The framework achieves high-frequency operation with an average latency of 9.05 ms, while over 95% of retargeted frames satisfy the safety criteria, effectively mitigating self-collision and penetration during complex manipulation tasks.
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）
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）
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）
EaDex: A Cross-Embodiment Dexterous Manipulation Framework from Low-Cost Demonstrations
Faculty of Robot Science and Engineering, Northeastern University（机器人科学与工程学院，东北大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究所，中国科学院）；School of Automation, Nanjing University of Information Science and Technology（自动化学院，南京信息科学技术大学）
KineDex: Learning Tactile-Informed Visuomotor Policies via Kinesthetic Teaching for Dexterous Manipulation
Tongji University（同济大学）；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；University of Hong Kong（香港大学）；Shanghai Qi Zhi Institute（上海启智研究院）；Shanghai AI Lab（上海人工智能实验室）
We propose to perform imitation learning for dexterous manipulation with multi-finger robot hand from human demonstrations, and transfer the policy to the real robot hand. We introduce a novel single-camera teleoperation system to collect the 3D demonstrations efficiently with only an iPad and a computer. One key contribution of our system is that we construct a customized robot hand for each user in the physical simulator, which is a manipulator resembling the same kinematics structure and shape of the operator's hand. This provides an intuitive interface and avoid unstable human-robot hand r…
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）
Function beyond Form: Functional Correspondence for Cross-Embodiment Dexterous Grasp Generation
Southern University of Science and Technology（南方科技大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；Shanghai Jiao Tong University（上海交通大学）；Rysen Robotics（睿森机器人）
