High-fidelity teleoperation of dexterous robotic hands is essential for bringing robots into unstructured domestic environments. However, existing teleoperation systems often face a trade-off between performance and portability: vision-based capture systems are constrained by costs and line-of-sight requirements, while mechanical exoskeletons are bulky and physically restrictive. In this paper, we present DexEMG, a lightweight and cost-effective teleoperation system leveraging surface electromyography (sEMG) to bridge the gap between human intent and robotic execution. We first collect a synchronized dataset of sEMG signals and hand poses via a MoCap glove to train EMG2Pose, a neural network capable of continuously predicting hand kinematics directly from muscle activity. To ensure seamless control, we develop a robust hand retargeting algorithm that maps the predicted poses onto a multi-fingered dexterous hand in real-time. Experimental results demonstrate that DexEMG achieves high precision in diverse teleoperation tasks. Notably, our system exhibits strong generalization capabilities across novel objects and complex environments without the need for intensive individual-specific recalibration. This work offers a scalable and intuitive interface for both general-purpose robotic manipulation and assistive technologies.
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）
UniBiDex: A Unified Teleoperation Framework for Robotic Bimanual Dexterous Manipulation
Department of Computer Science, The University of Hong Kong（香港大学计算机科学系）；Dongguan Key Laboratory of Intelligent Equipment and Smart Industry, School of Advanced Engineering, The Great Bay University（东莞智能装备与智能制造重点实验室，高级工程学院，大湾区大学）；Department of Mechanical Engineering, The Hong Kong Polytechnic University（香港理工大学机械工程系）；Guangdong Academy of Sciences（广东省科学院）
Universität Hamburg（汉堡大学）；Tsinghua University（清华大学）
In this paper, we present TeachNet, a novel neural network architecture for intuitive and markerless vision-based teleoperation of dexterous robotic hands. Robot joint angles are directly generated from depth images of the human hand that produce visually similar robot hand poses in an end-to-end fashion. The special structure of TeachNet, combined with a consistency loss function, handles the differences in appearance and anatomy between human and robotic hands. A synchronized human-robot training set is generated from an existing dataset of labeled depth images of the human hand and simulate…
This paper explores the possibility of improving bilateral robot manipulation task performance through optimizing the robot morphology and configuration of the system through motion. To optimize the design for different scenarios, we select a set of tasks that represent the variability in small scale manipulation (e.g. pick and place, tasks involving positioning and orientation) and track the motion to obtain a reproducible trajectory. Kinematic data is captured through an electromagnetic (EM) tracker system while a human subject performs the tasks. Then, the data is pre-processed and used to …
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）
Towards Human-Like Manipulation through RL-Augmented Teleoperation and Mixture-of-Dexterous-Experts VLA
Shanghai Jiao Tong University（上海交通大学）；Sharpa；Shanghai AI Lab（上海人工智能实验室）；Zhongguancun Academy（中关村学院）；National University of Singapore（新加坡国立大学）；Shanghai Innovation Institute（上海创新研究院）
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）
Hunan University（湖南大学）；Bielefeld University（比勒费尔德大学）；University of the West of England（西英格兰大学）
In this work, we focus on improving the robot's dexterous capability by exploiting visual sensing and adaptive force control. TeachNet, a vision-based teleoperation learning framework, is exploited to map human hand postures to a multi-fingered robot hand. We augment TeachNet, which is originally based on an imprecise kinematic mapping and position-only servoing, with a biomimetic learning-based compliance control algorithm for dexterous manipulation tasks. This compliance controller takes the mapped robotic joint angles from TeachNet as the desired goal, computes the desired joint torques. It…
Hand-in-the-Loop: Improving VLA Policies for Dexterous Manipulation via Seamless Hand-Arm Intervention
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University（机械系统与振动国家重点实验室，机械工程学院，上海交通大学）；Shanghai Key Laboratory of Intelligent Robotics, Meta Robotics Institute, Shanghai Jiao Tong University, Shanghai 200240, China（智能机器人上海市重点实验室，元机器人研究院，上海交通大学，上海200240，中国）；The University of Tokyo（东京大学）
In-hand pivoting is one of the important manipulation skills that leverage robot grippers' extrinsic dexterity to perform repositioning tasks to compensate for environmental uncertainties and imprecise motion execution. Although many researchers have been trying to solve pivoting problems using mathematical modeling or learning-based approaches, the problems remain as open challenges. On the other hand, humans perform in-hand manipulation with remarkable precision and speed. Hence, the solution could be provided by making full use of this intrinsic human skill through dexterous teleoperation. …
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）
Suction Leap-Hand: Suction Cups on a Multi-fingered Hand Enable Embodied Dexterity and In-Hand Teleoperation
School of Informatics, University of Edinburgh（信息学院，爱丁堡大学）；School of Physics, Engineering and Technology, University of York（物理、工程与技术学院，约克大学）；Tsinghua University（清华大学）
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）
This study tries to explain the connection between communication modalities and levels of supervision in teleoperation during a dexterous task, like surgery. This concept is applied to two surgical related tasks: incision and peg transfer. It was found that as the complexity of the task escalates, the combination linking human supervision with a more expressive modality shows better performance than other combinations of modalities and control. More specifically, in the peg transfer task, the combination of speech modality and action level supervision achieves shorter task completion time (77.…
