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

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

|Authors: 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

|Venue: arXiv preprint | Year: 2026

|arXiv: 2607.08751v1

Abstract

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 Contributions

• Building general-purpose dexterous manipulation policies requires benchmarks that go beyond isola…
• However, existing benchmarks remain limited in task and data diversity, embodiment coverage, or c…
• 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 grasp…
• It supports 3 robot arms and 6 dexterous hands, and is extensible to new tasks, assets, and embod…

Topics

• manipulation
• vision
• vla
• reinforcement-learning
• control
• learning-from-demonstration
• benchmark

Code & Data

No code repository linked in paper metadata.

BibTeX

@article{Yao2026_260708751v1,
  title     = {DexVerse: A Modular Benchmark for Multi-Task, Multi-Embodiment Dexterous Manipulation},
  author    = {Yunchao Yao and Zhuxiu Xu and Tianqi Zhang and Zixian Liu and Sikai Li and Zhenyu Wei and Feng Chen and Dihong Huang and Kechang Wan and Chenyang Ma and Shuqi Zhao and Shenghua Gao and Masayoshi Tomizuka and Yi Ma and Mingyu Ding},
  year      = {2026},
  eprint    = {2607.08751v1},
  archivePrefix = {arXiv},
  primaryClass  = {cs.RO},
  url       = {https://arxiv.org/abs/2607.08751v1}
}