DexVerse: A Modular Benchmark for Multi-Task, Multi-Embodiment Dexterous Manipulation

Yunchao Yao, Zhuxiu Xu, Tianqi Zhang, Zixian Liu, Sikai Li, Zhenyu Wei, Feng Chen, Dihong Huang, Kechang Wan, Chenyang Ma, Shuqi Zhao, Shenghua Gao, Masayoshi Tomizuka, Yi Ma, Mingyu Ding · N/A · 2026

Summary

Abstract Summary

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

Key Points

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.

DexVerse Environment

DexVerse is a large-scale modular simulation environment with 100 dexterous manipulation tasks. The task suite covers a broad spectrum of manipulation problems, ranging from primitive and functional object manipulation to non-prehensile and contact-rich interactions, bimanual coordination, multi-goal and long-horizon tasks.

Each environment specifies the scene, assets, observation interfaces, initialization distribution, success conditions, and optional visual or physical randomization.

DexVerse Environment — DexVerse: A Modular Benchmark for Multi-Task, Multi-Embodiment Dexterous Manipulation — Figure 2: Visualization of selected tasks from the DexVerse environments.

Dataset and Evaluation

Teleoperation Data Collection. We develop an embodiment-adaptive teleoperation pipeline to scale demonstration collection in DexVerse. The system uses Apple Vision Pro through Isaac Lab’s CloudXR-based XR teleoperation interface [ 38 ] , which streams simulation feedback to the headset and returns hand-tracking inputs for robot control.

The tracked human wrist pose is used as the target pose for the robot end-effector, and the robot arm follows this target through an inverse-kinematics controller.

Dataset and Evaluation — DexVerse: A Modular Benchmark for Multi-Task, Multi-Embodiment Dexterous Manipulation — Figure 3: Visualization of task progression of the 5 long-horizon tasks in DexVerse environments.

Sources and Demonstrations

Figures are reproduced from Yao et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.