Building general-purpose dexterous manipulation policies requires benchmarks that go beyond isolated tasks to systematically evaluate policies across diverse interaction modes, sensory conditions, and robot embodiments. However, existing benchmarks remain limited in task and data diversity, embodiment coverage, or controllable visual variation, hindering studies of cross-task and cross-embodiment generalization. We present DexVerse, a large-scale and modular benchmark for dexterous manipulation. DexVerse includes 100 tasks spanning a broad range of manipulation skills, including object grasping and relocation, articulated-object interaction, functional tool use, bimanual coordination, non-prehensile control, contact-rich behaviors, multi-goal execution, and long-horizon multi-stage task completion. It supports 3 robot arms and 6 dexterous hands, and is extensible to new tasks, assets, and embodiments. To evaluate visuomotor generalization, DexVerse provides configurable visual variations in textures, background, lighting, and camera viewpoints. We further provide a VR-based teleoperation interface and 3,180 demonstrations with synchronized proprioceptive, RGB, depth, point-cloud, and state observations. We benchmark representative methods, including Diffusion Policy, DP3, OpenVLA, and $π_{0.5}$, across 19 tasks. Results reveal substantial challenges in task generalization and visuomotor robustness, establishing DexVerse as a promising testbed for general-purpose dexterous manipulation. Project page: https://ycyao216.github.io/DexVerse.site
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）
Cross-Embodiment Robot Manipulation via a Unified Hand Action Space
Intelligent Robotics and Vision Lab, University of Texas at Dallas（德克萨斯大学达拉斯分校智能机器人与视觉实验室）；Intelligent Robotics and Interactive Systems Lab, Arizona State University（亚利桑那州立大学智能机器人与交互系统实验室）
$\mathcal{D(R,O)}$ Grasp: A Unified Representation of Robot and Object Interaction for Cross-Embodiment Dexterous Grasping
National University of Singapore（新加坡国立大学）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；Zhiyuan College, Shanghai Jiao Tong University（上海交通大学致远学院）
MachaGrasp: Morphology-Aware Cross-Embodiment Dexterous Hand Articulation Generation for Grasping
Robotics & Autonomous Systems Division, Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR-I 2 R)（机器人与自主系统 division，信息通信研究所，科技研究局）；College of Computing and Data Science, Nanyang Technological University（计算与数据科学学院，南洋理工大学）；Show Lab, National University of Singapore（Show Lab，国立新加坡大学）
SiMDex: Mining Similar Egocentric Videos for Cross-Embodiment Dexterous Manipulation
The University of Tokyo（东京大学）；ByteDance Seed（字节跳动 Seed）；The University of Hong Kong（香港大学）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）
Structural Action Transformer for 3D Dexterous Manipulation
MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China（脑启发智能感知与认知国家重点实验室，中国科学技术大学）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（人工智能研究院，合肥综合性国家科学中心）
FoLD: Force-Informed Learning for Dexterous Articulated Object Manipulation
University of Science and Technology of China（中国科学技术大学）；Institute of AI for Industries, Chinese Academy of Sciences（中国科学院人工智能产业研究院）；Shenzhen University（深圳大学）
Towards Robotic Dexterous Hand Intelligence: A Survey
University of Liverpool（利物浦大学）；Xi’an Jiaotong-Liverpool University（西安交通大学-利物浦大学）；Duke Kunshan University（杜克-昆山大学）；The Chinese University of Hong Kong（香港中文大学）
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）
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）
The Hong Kong Polytechnic University（香港理工大学）；Technical University of Denmark（丹麦技术大学）
While the original goal for developing robots is replacing humans in dangerous and tedious tasks, the final target shall be completely mimicking the human cognitive and motor behaviour. Hence, building detailed computational models for the human brain is one of the reasonable ways to attain this. The cerebellum is one of the key players in our neural system to guarantee dexterous manipulation and coordinated movements as concluded from lesions in that region. Studies suggest that it acts as a forward model providing anticipatory corrections for the sensory signals based on observed discrepanci…
AutoBio+: Benchmarking Dexterous Manipulation with Dynamic Experimental Phenomena in Realistic Laboratories
Shanghai AI Laboratory（上海人工智能实验室）；Huazhong University of Science and Technology（华中科技大学）；Shanghai Jiao Tong University（上海交通大学）；University of Southern California（南加州大学）
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）
