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论文arXiv 2502.077302025-02-11

DOGlove开源600美元21自由度力反馈遥操作手套支持模仿学习 ​

针对灵巧手遥操作高成本与感知缺失痛点，研究提出DOGlove系统。该手套造价低于600美元，集成21-DoF动捕、5-DoF力反馈及触觉致动器，支持无视觉任务执行与模仿学习策略训练，软硬件全开源。

AI 整理
  • 本体 SDK 与控制
  • 数据与采集

本文由 AI 根据公开内容整理，可能有误，请以原文为准：DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove（arXiv 2502.07730）

核心内容 ​

针对机器人灵巧操作遥操作设备昂贵且缺乏多模态反馈的行业瓶颈，arXiv论文发布DOGlove低成本开源力反馈手套系统。该装置组装仅需数小时，总物料成本控制在600美元以内，专为高精度遥操作与具身数据采集设计。实验表明其在复杂接触任务中成功率高，并验证了利用采集数据训练模仿学习策略的有效性，为具身智能数据获取提供高性价比方案。

硬件架构采用定制关节结构实现21-DoF运动捕捉，通过紧凑型缆绳驱动扭矩传输机制提供5-DoF多向力反馈，指尖集成线性谐振致动器实现5-DoF触觉反馈。软件层面应用动作与触觉力重定向算法，确保人手到机械手的精准映射。系统在无视觉反馈场景下验证了力觉对任务性能的关键作用，相关硬件设计与控制代码已在项目主页完全开源，降低了灵巧操作研究门槛。

关键看点 ​

  • 低成本硬件：总物料成本低于600美元，组装耗时仅数小时，大幅降低灵巧手遥操作与数据采集门槛
  • 多维感知反馈：集成21-DoF动捕、5-DoF缆驱动力反馈及指尖线性谐振致动器，实现沉浸式操控
  • 无视觉作业验证：在无视觉反馈场景中证实力触觉对复杂接触任务的关键作用，提升操作鲁棒性
  • 模仿学习闭环：利用遥操作采集的演示数据成功训练模仿学习策略，验证了作为具身数据源的有效性

工程与产业观察 ​

DOGlove以极低BOM成本解决了灵巧手遥操作中力觉反馈缺失与设备昂贵的双重难题。其开源特性与模仿学习验证，使其成为构建高质量具身操作数据集的高性价比基础设施，有助于加速非结构化环境下灵巧操作算法的迭代与泛化研究。

英文摘要 (Abstract) ​

Dexterous hand teleoperation plays a pivotal role in enabling robots to achieve human-level manipulation dexterity. However, current teleoperation systems often rely on expensive equipment and lack multi-modal sensory feedback, restricting human operators' ability to perceive object properties and perform complex manipulation tasks. To address these limitations, we present DOGlove, a low-cost, precise, and haptic force feedback glove system for teleoperation and manipulation. DoGlove can be assembled in hours at a cost under 600 USD. It features a customized joint structure for 21-DoF motion capture, a compact cable-driven torque transmission mechanism for 5-DoF multidirectional force feedback, and a linear resonate actuator for 5-DoF fingertip haptic feedback. Leveraging action and haptic force retargeting, DOGlove enables precise and immersive teleoperation of dexterous robotic hands, achieving high success rates in complex, contact-rich tasks. We further evaluate DOGlove in scenarios without visual feedback, demonstrating the critical role of haptic force feedback in task performance. In addition, we utilize the collected demonstrations to train imitation learning policies, highlighting the potential and effectiveness of DOGlove. DOGlove's hardware and software system will be fully open-sourced at https://do-glove.github.io/.

作者：Han Zhang, Songbo Hu, Zhecheng Yuan, Huazhe Xu

来源

本页事实对应的公开页面，以原文为准。

  1. arXiv 2502.07730：DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove访问 2026-10-23