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CDF-Glove: A Cable-Driven Force Feedback Glove for Dexterous Teleoperation
This paper presents CDF-Glove, a low-cost, lightweight, cable-driven force-feedback glove for dexterous teleoperation, aimed at improving demonstration data quality and supporting imitation learning. Its core value lies…
dexterous-teleoperationhaptic-feedbackforce-feedback-gloveimitation-learningdiffusion-policy
Summary
This paper presents CDF-Glove, a low-cost, lightweight, cable-driven force-feedback glove for dexterous teleoperation, aimed at improving demonstration data quality and supporting imitation learning. Its core value lies in combining high-dimensional hand tracking with tactile/force feedback while keeping the cost down to about US$230 and releasing the design as open source.
Problem
- Imitation learning in dexterous manipulation relies heavily on high-quality teleoperation demonstrations, but existing gloves often lack tactile feedback, making it difficult for operators to correct finger posture in real time based on contact state.
- Existing high-DoF tactile gloves usually make trade-offs among high price, large size, weak feedback, and poor wearability, which is unfavorable for long-duration data collection.
- If demonstration quality is low, the success rate and efficiency of trained policies are both limited, so better teleoperation interfaces are important for dexterous manipulation data collection.
Approach
- Designed a cable-driven force-feedback glove: components are integrated on the back of the hand, and the fingers use steel cables/PTFE sheaths for transmission, balancing light weight, safety, and ease of replication.
- The glove provides 20 hand DoF states: 16 measured directly and 4 inferred through kinematic coupling; it also combines with HTC Vive for 6D wrist tracking.
- Proposed a kinematic model from encoder displacement to finger joint angles: directly measures MCP/DIP, uses the DIP-PIP coupling relationship to infer PIP, and then maps to different dexterous hands.
- Proposed a force-feedback tracking model: computes cable length changes in real time from finger joint angles, and servo motors reel cables in/out to maintain tension; it also introduces a bimodal feedback strategy, using LRA vibration in the low-force range and cable-driven resistive force feedback in the high-force range.
- Used this system to collect bimanual teleoperation datasets and trained Diffusion Policy baselines, comparing them with policies trained on kinesthetic teaching data.
Results
- Hardware metrics: glove weight 0.49 kg, maximum sampling frequency about 100 Hz, capable of measuring 16 DoF with 4 DoF inferred through coupling, and force-feedback latency to the hand of about 200 ms.
- Accuracy metrics: in the index finger DIP repeatability experiment, the mean contact angle was 63.15° with a standard deviation of 0.29°; the authors claim distal-joint repeatability of < 0.4° / about 0.4°, and other MCP/PIP tests also remained below 0.4°.
- Cost metrics: total cost is about $230.51, lower than the DOGlove $600 and GEX Series $600 cited in the paper.
- Force-feedback effectiveness: in the water-bottle grasping experiment, under blindfolded + noise-reduction conditions, the success rate improved from 1/10 (10%) to 5/10 (50%), a 4× improvement, and average completion time dropped from 18.30 s to 8.52 s; under conditions without sensory occlusion, success rate improved from 7/10 (70%) to 9/10 (90%), and completion time decreased from 3.11 s to 2.51 s.
- Imitation learning results: compared with kinesthetic teaching, policies trained on CDF-Glove teleoperation demonstrations achieved an average success rate improvement of 55%, and reduced average completion time by about 15.2 s, i.e. a 47.2% relative reduction.
- Generalization claim: the authors state that their kinematics and control stack have been validated on multiple dexterous hands with different kinematics/DoF, and that the code and hardware designs are open sourced; however, the excerpt does not provide more detailed cross-platform quantitative tables.
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