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

TL;DR

DOGlove introduces a low-cost, open-source haptic glove enabling precise teleoperation of dexterous robotic hands via 21-DoF motion capture and 5-DoF haptic feedback. It combines a cable-driven force transmission and fingertip LRAs within a compact, modular design, and supports action and haptic force retargeting to bridge embodiment gaps. Through user studies, bottle-slipping, carton-rotation, and imitation-learning experiments, DOGlove demonstrates improved perception and manipulation performance, even without visual feedback, and effectively collects demonstrations for DP3-based policies. The work emphasizes accessibility and reproducibility by open-sourcing hardware, software, and simulation assets. Overall, DOGlove offers a practical, scalable platform for immersive teleoperation and data-driven dexterous manipulation.

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/.

Figures (10)

• Figure 1: DOGlove, a haptic force feedback glove designed for precise teleoperation and dexterous manipulation. It features 21-DoF motion capture and 5-DoF haptic force feedback. By leveraging action and force retargeting, it enables the teleoperation of dexterous hands for complex, contact-rich tasks, including: a) without visual feedback, adjusting contact force with a bottle during teleoperation, b) regulating the flow of condensed milk, and c) performing in-hand rotation by using haptic force feedback to adjust friction.
• Figure 2: Teleoperation demos. a) While squeezing condensed milk, the operator regulates the flow using haptic force feedback from DOGlove. b) The operator grasps a slipping bottle without visual feedback. c) The user identifies object pairs solely through haptic force feedback.
• Figure 3: The kinematic structure of DOGlove, designed to replicate the kinematics of a human hand. The MCP (B+S) and TM (B+S) joints are modeled as ball joints using a combination of two rotary joints. The right figure from cerulo2017teleoperation illustrates the simplified human hand kinematics.
• Figure 4: Improper linkage lengths can cause collisions between the human finger (link $\boldsymbol{f, g}$) and the glove (link $\boldsymbol{m}$), restricting finger movements and leading to discomfort and poor MoCap performance.
• Figure 5: Exploded view of the finger assembly, with the highlighted area indicating the basic components of a rotary joint.