DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force Feedback Glove

2025-02-11

AI Summary

Existing hand teleoperation systems rely on expensive hardware and lack multimodal sensory feedback, limiting operators’ perception of object properties and performance in complex contact-rich tasks. This paper introduces a low-cost (<$600), open-source, high-precision haptic force-feedback glove. It integrates 21-degree-of-freedom motion tracking, 5-degree-of-freedom bidirectional force feedback, and 5-degree-of-freedom linear-resonant tactile feedback at the fingertips. We propose a novel monolithic lightweight design incorporating custom joints, compact tendon-driven torque transmission, and miniaturized actuators. Furthermore, we introduce a motion–haptics co-remapping strategy and—critically—demonstrate for the first time that force feedback alone (without vision) is decisive for task success. Experiments confirm robust performance and high success rates in contact-intensive manipulation. Demonstrated trajectories successfully train effective imitation learning policies. The complete hardware design and software stack are fully open-sourced.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Interaction Techniques and Devices
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

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/.

Problem

• Develop low-cost haptic feedback glove
• Enable precise robotic hand teleoperation
• Enhance manipulation with multi-modal feedback

Innovation

• Low-cost haptic feedback glove
• 21-DoF motion capture system
• Immersive teleoperation with force feedback