DexVerse: A Modular Benchmark for Multi-Task, Multi-Embodiment Dexterous Manipulation
- URL: http://arxiv.org/abs/2607.08751v1
- Date: Thu, 09 Jul 2026 17:50:47 GMT
- Title: DexVerse: A Modular Benchmark for Multi-Task, Multi-Embodiment Dexterous Manipulation
- Abstract summary: We present DexVerse, a large-scale and modular benchmark for dexterous manipulation.<n>We benchmark representative methods, including Diffusion Policy, DP3, OpenVLA, and $_0.5$, across 19 tasks.<n>Results reveal substantial challenges in task generalization and visuomotor robustness.
- Score: 66.68529423691461
- License: http://creativecommons.org/licenses/by/4.0/
- 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
Related papers
- RoboTacDex: A Dexterous Visual-Tactile-Action Dataset for Humanoid Manipulation [29.49648908136145]
RoboTacDex is a large, multi-modal, and diverse dataset of dexterous manipulation behaviors performed with a humanoid robot.<n>Built on the publicly accessible humanoid robot Unitree G1, RoboTacDex consists of 6k trajectories covering 19 tasks, 23 skills, and interactions with 22 objects.
arXiv Detail & Related papers (2026-06-30T15:39:29Z) - Task Editing for Generalizable 3D Visuomotor Policy Learning [50.590696755853365]
3D visuomotor policies offer a promising direction for complex robotic manipulation, as depth maps and point clouds provide rich geometric information for spatial reasoning.<n>Existing methods commonly use demonstration generation strategies to improve data efficiency by applying object-centric transformations to human-collected demonstrations.<n>We propose Task-Edit, a novel demonstration generation framework that generates diverse trajectories from a task-centric editing perspective.
arXiv Detail & Related papers (2026-06-05T07:54:49Z) - DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo [82.73355932907565]
DexJoCo is a benchmark and toolkit for task-oriented dexterous manipulation.<n>We present 11 functionally grounded tasks that evaluate tool-use, bimanual coordination, long-horizon execution, and reasoning.<n>We benchmark modern models under diverse settings, including visual and dynamics randomization, multi-task training, and action-head adaptation.
arXiv Detail & Related papers (2026-05-15T17:59:51Z) - VTC-Bench: Evaluating Agentic Multimodal Models via Compositional Visual Tool Chaining [49.78591189918702]
We introduce VisualToolChain-Bench (VTC-Bench) to evaluate tool-use proficiency in Multimodal Large Language Models (MLLMs)<n>To align with realistic computer vision pipelines, our framework features 32 diverse OpenCV-based visual operations.<n>For precise evaluation, we provide 680 curated problems structured across a nine-category cognitive hierarchy.
arXiv Detail & Related papers (2026-03-16T09:31:44Z) - Concurrent Prehensile and Nonprehensile Manipulation: A Practical Approach to Multi-Stage Dexterous Tasks [23.52755440099898]
We present DexMulti, a sample-efficient approach for real-world dexterous multi-task manipulation.<n>Rather than learning monolithic policies, our method retrieves demonstrated skills based on current object geometry.<n>Our approach achieves an average success rate of 66% on training objects with only 3-4 demonstrations per object, outperforming diffusion policy baselines by 2-3x.
arXiv Detail & Related papers (2026-03-12T08:25:21Z) - TacUMI: A Multi-Modal Universal Manipulation Interface for Contact-Rich Tasks [35.05859151174601]
We build on the idea of the handheld demonstration device Universal Manipulation Interface (UMI)<n>We introduce TacUMI, a multi-modal data collection system that integrates ViTac sensors, force-torque sensor, and pose tracker into a compact, robot-compatible gripper design.<n>We then propose a multi-modal segmentation framework that leverages temporal models to detect semantically meaningful event boundaries.
arXiv Detail & Related papers (2026-01-21T00:14:28Z) - SkillBlender: Towards Versatile Humanoid Whole-Body Loco-Manipulation via Skill Blending [79.83865372778273]
We introduce SkillBlender, a novel hierarchical reinforcement learning framework for versatile humanoid loco-manipulation.<n>SkillBlender first pretrains goal-conditioned task-agnostic primitive skills, and then dynamically blends these skills to accomplish complex loco-manipulation tasks.<n>We also introduce SkillBench, a parallel, cross-embodiment, and diverse simulated benchmark containing three embodiments, four primitive skills, and eight challenging loco-manipulation tasks.
arXiv Detail & Related papers (2025-06-11T03:24:26Z) - What Limits Virtual Agent Application? OmniBench: A Scalable Multi-Dimensional Benchmark for Essential Virtual Agent Capabilities [56.646832992178105]
We introduce OmniBench, a cross-platform, graph-based benchmark with an automated pipeline for synthesizing tasks of controllable complexity.<n>We present OmniEval, a multidimensional evaluation framework that includes subtask-level evaluation, graph-based metrics, and comprehensive tests across 10 capabilities.<n>Our dataset contains 36k graph-structured tasks across 20 scenarios, achieving a 91% human acceptance rate.
arXiv Detail & Related papers (2025-06-10T15:59:38Z) - Diffusion Transformer Policy [48.50988753948537]
We propose a large multi-modal diffusion transformer, dubbed as Diffusion Transformer Policy, to model continuous end-effector actions.<n>By leveraging the scaling capability of transformers, the proposed approach can effectively model continuous end-effector actions across large diverse robot datasets.
arXiv Detail & Related papers (2024-10-21T12:43:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.