ArXiv

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

2026-07-09 · 17:50 UTC ·Yunchao Yao, Zhuxiu Xu, Tianqi Zhang... ·1 min read
Authors
Yunchao Yao, Zhuxiu Xu, Tianqi Zhang...
Categories
cs.RO
arXiv
https://arxiv.org/abs/2607.08751v1
PDF
https://arxiv.org/pdf/2607.08751v1

Brief

DexVerse is a modular benchmark designed to evaluate multi-task, multi-embodiment dexterous manipulation by providing 100 diverse tasks (grasping, articulated interaction, tool use, bimanual coordination, non-prehensile and contact-rich behaviors), three robot arms, six hands, configurable visual variations, and 3,180 VR-collected demonstrations with multimodal sensing. Benchmarks of Diffusion Policy, DP3, OpenVLA, and π_{0.5} across 19 tasks reveal notable gaps in cross-task and visuomotor generalization, highlighting the suite's value for advancing general-purpose dexterous controllers.

Why it matters

DexVerse is a large-scale modular benchmark for dexterous manipulation containing 100 tasks, supporting 3 robot arms and 6 dexterous hands, and providing 3,180 teleoperated demonstrations with synchronized proprioceptive, RGB, depth, point-cloud, and state observations.

Key details

  • DexVerse offers configurable visual variation (textures, backgrounds, lighting, camera viewpoints) and VR-based teleoperation to evaluate visuomotor robustness and cross-embodiment generalization.
  • The authors benchmarked representative methods (Diffusion Policy, DP3, OpenVLA, and π_{0.5}) across 19 tasks and report substantial challenges in task generalization and visuomotor robustness, positioning DexVerse as a stress-test for general-purpose dexterous policies.
Source evidence

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