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GelSight Svelte Hand: A Three-finger, Two-DoF, Tactile-rich, Low-cost Robot Hand for Dexterous Manipulation

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arxiv 2309.10886 v1 pith:7CE2CBGX submitted 2023-09-19 cs.RO

Jialiang Zhao , Edward H. Adelson This is my paper · ORCID
classification cs.RO
keywords handfingergelsightsveltegraspstactilesensingalanz
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This paper presents GelSight Svelte Hand, a novel 3-finger 2-DoF tactile robotic hand that is capable of performing precision grasps, power grasps, and intermediate grasps. Rich tactile signals are obtained from one camera on each finger, with an extended sensing area similar to the full length of a human finger. Each finger of GelSight Svelte Hand is supported by a semi-rigid endoskeleton and covered with soft silicone materials, which provide both rigidity and compliance. We describe the design, fabrication, functionalities, and tactile sensing capability of GelSight Svelte Hand in this paper. More information is available on our website: \url{https://gelsight-svelte.alanz.info}.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Grasp EveryThing (GET): 1-DoF, 3-Fingered Gripper with Tactile Sensing for Robust Grasping

    cs.RO 2025-05 conditional novelty 6.0 of 10

    A 1-DoF three-finger gripper with V-shaped fingers and rigid fingernails grasps a wider range of objects more securely than flat two-finger grippers, with optional camera-based tactile force sensing.

  2. PolyTouch: A Robust Multi-Modal Tactile Sensor for Contact-rich Manipulation Using Tactile-Diffusion Policies

    cs.RO 2025-04 conditional novelty 6.0 of 10

    A multi-modal tactile finger with a VHB-tape elastomer lasts 35 hours in wear tests and its tactile-diffusion policy beats haptic-oblivious policies in four bimanual manipulation tasks.

  3. TwinTac: A Wide-Range, Highly Sensitive Tactile Sensor with Real-to-Sim Digital Twin Sensor Model

    cs.RO 2025-09 conditional novelty 5.0 of 10

    A tactile sensor made from eight barometer chips reads forces from 0.01 N to over 200 N, and a learned FEM-to-signal model generates simulated tactile data that lifts shape classification accuracy from 33.6% to 95%.

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