DexEXO: A Wearability-First Dexterous Exoskeleton for Operator-Agnostic Demonstration and Learning

• URL: http://arxiv.org/abs/2603.17323v1
• Date: Wed, 18 Mar 2026 03:37:13 GMT
• Title: DexEXO: A Wearability-First Dexterous Exoskeleton for Operator-Agnostic Demonstration and Learning
• Abstract summary: DexEXO is a wearability-first hand exoskeleton that aligns visual appearance, contact geometry, and kinematics at the hardware level.<n>A passive hand visually matches the deployed robot, allowing direct policy training from raw wrist-mounted RGB observations.
• Score: 9.237429778415226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Scaling dexterous robot learning is constrained by the difficulty of collecting high-quality demonstrations across diverse operators. Existing wearable interfaces often trade comfort and cross-user adaptability for kinematic fidelity, while embodiment mismatch between demonstration and deployment requires visual post-processing before policy training. We present DexEXO, a wearability-first hand exoskeleton that aligns visual appearance, contact geometry, and kinematics at the hardware level. DexEXO features a pose-tolerant thumb mechanism and a slider-based finger interface analytically modeled to support hand lengths from 140~mm to 217~mm, reducing operator-specific fitting and enabling scalable cross-operator data collection. A passive hand visually matches the deployed robot, allowing direct policy training from raw wrist-mounted RGB observations. User studies demonstrate improved comfort and usability compared to prior wearable systems. Using visually aligned observations alone, we train diffusion policies that achieve competitive performance while substantially simplifying the end-to-end pipeline. These results show that prioritizing wearability and hardware-level embodiment alignment reduces both human and algorithmic bottlenecks without sacrificing task performance. Project Page: https://dexexo-research.github.io/

Related papers

• AdvDex: Learning Dexterous Manipulation from Human Demonstrations via Joint-Aligned Actions and Adversarial Learning [25.241504281004243]
  We present AdvDex, a framework for learning dexterous manipulation from human and robot demonstrations.<n>First, we introduce OmniShare, a large-scale multimodal dataset of human manipulation demonstrations.<n>Second, we propose the Joint-Aligned Action Space (JAAS), a canonical action representation comprising an $mathrmSE(3)$ wrist pose and 15 finger joints.<n>Third, we use domain-adversarial learning to reduce embodiment-specific information in the learned visual representation.
  arXiv  Detail & Related papers  (2026-08-14T07:19:06Z)
• DexVerse: A Modular Benchmark for Multi-Task, Multi-Embodiment Dexterous Manipulation [66.68529423691461]
  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.
  arXiv  Detail & Related papers  (2026-07-09T17:50:47Z)
• RealDexUMI: A Wearable Universal Manipulation Interface for Dexterous Robot Learning [21.875676616617838]
  We present RealDexUMI, a wearable universal manipulation interface built around a shared dexterous end-effector module.<n>A palm-side is teleoperation maps human finger inputs to robot-hand commands, enabling real-time, glove-free, intuitive, and precise hand control.
  arXiv  Detail & Related papers  (2026-06-04T11:28:23Z)
• ActiveGlasses: Learning Manipulation with Active Vision from Ego-centric Human Demonstration [51.69384671495837]
  ActiveGlasses is a system for learning robot manipulation from ego-centric human demonstrations with active vision.<n>A stereo camera mounted on smart glasses serves as the sole perception device for both data collection and policy inference.<n>To enable zero-transfer, we extract object trajectories from demonstrations and use an object-centric point-cloud policy to jointly predict manipulation and head movement.
  arXiv  Detail & Related papers  (2026-04-09T17:59:08Z)
• HEX: Humanoid-Aligned Experts for Cross-Embodiment Whole-Body Manipulation [74.34984994596813]
  HEX is a state-centric framework for coordinated manipulation on humanoid robots.<n>It incorporates a humanoid-aligned universal state representation for scalable learning across heterogeneous embodiments.<n>It achieves state-of-the-art performance in task success rate and generalization.
  arXiv  Detail & Related papers  (2026-04-09T09:01:43Z)
• DexViTac: Collecting Human Visuo-Tactile-Kinematic Demonstrations for Contact-Rich Dexterous Manipulation [4.463599051210204]
  Large-scale, high-quality multimodal demonstrations are essential for robot learning of contact-rich dexterous manipulation.<n>We present DexViTac, a portable, human-centric data collection system tailored for contact-rich dexterous manipulation.
  arXiv  Detail & Related papers  (2026-03-18T15:39:58Z)
• DexHiL: A Human-in-the-Loop Framework for Vision-Language-Action Model Post-Training in Dexterous Manipulation [14.050551792714083]
  Human-in-the-Loop (HiL) learning has proven to be a powerful mechanism for refining robot policies.<n>We present DexHiL, the first integrated arm-hand human-in-the-loop framework for dexterous VLA models.
  arXiv  Detail & Related papers  (2026-03-10T02:55:27Z)
• Dexplore: Scalable Neural Control for Dexterous Manipulation from Reference-Scoped Exploration [58.4036440289082]
  Hand-object motion-capture (MoCap) offer large-scale, contact-rich demonstrations and hold promise for dexterous robotic scopes.<n>We introduce Dexplore, a unified single-loop optimization that performs repositories and tracking to learn robot control policies directly from MoCap at scale.
  arXiv  Detail & Related papers  (2025-09-11T17:59:07Z)
• Being-H0: Vision-Language-Action Pretraining from Large-Scale Human Videos [66.62109400603394]
  We introduce Being-H0, a dexterous Vision-Language-Action model trained on large-scale human videos.<n>Our approach centers on physical instruction tuning, a novel training paradigm that combines large-scale VLA pretraining from human videos, physical space alignment for 3D reasoning, and post-training adaptation for robotic tasks.<n>We empirically show the excellence of Being-H0 in hand motion generation and instruction following, and it also scales well with model and data sizes.
  arXiv  Detail & Related papers  (2025-07-21T13:19:09Z)
• TeleOpBench: A Simulator-Centric Benchmark for Dual-Arm Dexterous Teleoperation [50.261933845325636]
  We introduce TeleOpBench, a simulator-centric benchmark tailored to bimanual dexterous teleoperation.<n>Within this benchmark we implement four representative teleoperation modalities-(i) MoCap, (ii) VR device, (iii) arm-hand exoskeletons, and (iv) monocular vision tracking.
  arXiv  Detail & Related papers  (2025-05-19T06:08:53Z)
• One-Shot Imitation under Mismatched Execution [7.060120660671016]
  Human demonstrations are a powerful way to program robots to do long-horizon manipulation tasks.<n> translating these demonstrations into robot-executable actions presents significant challenges due to execution mismatches in movement styles and physical capabilities.<n>We propose RHyME, a novel framework that automatically pairs human and robot trajectories using sequence-level optimal transport cost functions.
  arXiv  Detail & Related papers  (2024-09-10T16:11:57Z)
• What Makes Pre-Trained Visual Representations Successful for Robust Manipulation? [57.92924256181857]
  We find that visual representations designed for manipulation and control tasks do not necessarily generalize under subtle changes in lighting and scene texture. We find that emergent segmentation ability is a strong predictor of out-of-distribution generalization among ViT models.
  arXiv  Detail & Related papers  (2023-11-03T18:09:08Z)
• What Matters to You? Towards Visual Representation Alignment for Robot Learning [81.30964736676103]
  When operating in service of people, robots need to optimize rewards aligned with end-user preferences. We propose Representation-Aligned Preference-based Learning (RAPL), a method for solving the visual representation alignment problem.
  arXiv  Detail & Related papers  (2023-10-11T23:04:07Z)
• Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis [4.297853208735328]
  Eye-in-hand systems automatically control the hand pre-shaping before the grasp.<n>We present an eye-in-hand learning-based approach for hand pre-shape classification from RGB sequences.
  arXiv  Detail & Related papers  (2022-03-18T09:16:48Z)
• Visual Imitation Made Easy [102.36509665008732]
  We present an alternate interface for imitation that simplifies the data collection process while allowing for easy transfer to robots. We use commercially available reacher-grabber assistive tools both as a data collection device and as the robot's end-effector. We experimentally evaluate on two challenging tasks: non-prehensile pushing and prehensile stacking, with 1000 diverse demonstrations for each task.
  arXiv  Detail & Related papers  (2020-08-11T17:58:50Z)

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.