CDF-Glove: A Cable-Driven Force Feedback Glove for Dexterous Teleoperation

2026-03-06

AI Summary

This work addresses the limitations of high-fidelity teleoperated demonstration in dexterous manipulation imitation learning, which are primarily hindered by the absence of tactile feedback and the bulkiness and high cost of existing systems. The authors propose a lightweight, low-cost (approximately \$230) cable-driven force-feedback glove supporting 20 degrees of freedom in the fingers—16 actively sensed and 4 passively coupled. Leveraging a kinematic model and a real-time force-feedback control stack, the system achieves high-precision interaction with a force-feedback latency of approximately 200 ms and a distal joint repeatability of 0.4°. Experimental results demonstrate that the glove improves teleoperation task success rates by a factor of four and reduces execution time by 47.2%. Furthermore, policies trained on data collected with this glove exhibit an average success rate improvement of 55%.

Technology Category

• Intelligent Robots: Manipulation
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning
• Humans and AI: Interaction Techniques and Devices

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

High-quality teleoperated demonstrations are a primary bottleneck for imitation learning (IL) in dexterous manipulation. However, haptic feedback provides operators with real-time contact information, enabling real-time finger posture adjustments, and thereby improving demonstration quality. Existing dexterous teleoperation platforms typically omit haptic feedback and remain bulky and expensive. We introduce CDF-Glove, a lightweight and low cost cable-driven force-feedback glove. The real-time state is available for 20 finger degrees of freedom (DoF), of which 16 are directly sensed and 4 are passively coupled (inferred from kinematic constraints). We develop a kinematic model and control stack for the glove, and validate them across multiple robotic hands with diverse kinematics and DoF. The CDF-Glove achieves distal joint repeatability of 0.4 degrees, and delivers about 200 ms force feedback latency, yielding a 4x improvement in task success rate relative to no-feedback teleoperation. We collect two bimanual teleoperation datasets, on which we train and evaluate Diffusion Policy baselines. Compared to kinesthetic teaching, the policies trained in our teleoperated demonstrations increase the average success rate by 55% and reduce the mean completion time by approximately 15.2 seconds (a 47.2% relative reduction). In particular, the CDF-Glove costs approximately US$230. The code and designs are released as open source at https://cdfglove.github.io/.

Problem

• dexterous teleoperation
• haptic feedback
• imitation learning
• force feedback glove
• teleoperated demonstrations

Innovation

• force feedback
• dexterous teleoperation
• cable-driven glove
• imitation learning
• kinematic modeling