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Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing

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arxiv 2407.07885 v2 pith:5FCLHFLA submitted 2024-07-10 cs.RO cs.LG

Jessica Yin , Haozhi Qi , Jitendra Malik , James Pikul , Mark Yim , Tess Hellebrekers This is my paper · ORCID
classification cs.ROcs.LG
keywords tactileforcesnormalonlysensingsheardexterousin-hand
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Recent progress in reinforcement learning (RL) and tactile sensing has significantly advanced dexterous manipulation. However, these methods often utilize simplified tactile signals due to the gap between tactile simulation and the real world. We introduce a sensor model for tactile skin that enables zero-shot sim-to-real transfer of ternary shear and binary normal forces. Using this model, we develop an RL policy that leverages sliding contact for dexterous in-hand translation. We conduct extensive real-world experiments to assess how tactile sensing facilitates policy adaptation to various unseen object properties and robot hand orientations. We demonstrate that our 3-axis tactile policies consistently outperform baselines that use only shear forces, only normal forces, or only proprioception. Website: https://jessicayin.github.io/tactile-skin-rl/

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Forward citations

Cited by 6 Pith papers

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

  1. LocoTouch: Learning Dynamic Quadrupedal Transport with Tactile Sensing

    cs.RO 2025-05 conditional novelty 7.0 of 10

    LocoTouch trains a quadrupedal policy that uses a 221-taxel tactile back to balance and transport unsecured cylindrical objects, transferring zero-shot to a real Unitree Go1.

  2. eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures

    cs.RO 2025-06 conditional novelty 6.0 of 10

    eFlesh is a customizable 3D-printed magnetic tactile sensor that localizes contact to 0.5 mm, estimates force within 0.27 N, and boosts precise robot manipulation success to 91%.

  3. Adaptive Visuo-Tactile Fusion with Predictive Force Attention for Dexterous Manipulation

    cs.RO 2025-05 conditional novelty 6.0 of 10

    A force-guided attention module and future-force prediction auxiliary task improve visuo-tactile fusion for dexterous manipulation, reaching 93% average success in real robot trials.

  4. From Simple to Complex Skills: The Case of In-Hand Object Reorientation

    cs.RO 2025-01 conditional novelty 6.0 of 10

    A hierarchical policy that picks among pre-trained single-axis rotation skills and adds residual corrections reorients diverse real-world objects with less tuning and more robustness than training from scratch.

  5. AdaDexGrasp: Adaptive Dexterous Grasping via 3D Visuo-Tactile Representation Fusion

    cs.RO 2026-08 conditional novelty 5.0 of 10

    AdaDexGrasp learns to fuse point clouds with finger-level tactile labels to generate, judge, and correct dexterous grasps, reporting 91%/82%/83% success on seen, unseen-object, and unseen-category sets in simulation.

  6. 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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