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AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touch

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arxiv 2405.07391 v3 pith:OTYOX5KY submitted 2024-05-12 cs.RO cs.AIcs.LG

AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touch

Max Yang , Chenghua Lu , Alex Church , Yijiong Lin , Chris Ford , Haoran Li , Efi Psomopoulou , David A.W. Barton
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Nathan F. Lepora
This is my paper
classification cs.RO cs.AIcs.LG
keywords policytactileanyrotatehandin-handrichrotationsim-to-real
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Human hands are capable of in-hand manipulation in the presence of different hand motions. For a robot hand, harnessing rich tactile information to achieve this level of dexterity still remains a significant challenge. In this paper, we present AnyRotate, a system for gravity-invariant multi-axis in-hand object rotation using dense featured sim-to-real touch. We tackle this problem by training a dense tactile policy in simulation and present a sim-to-real method for rich tactile sensing to achieve zero-shot policy transfer. Our formulation allows the training of a unified policy to rotate unseen objects about arbitrary rotation axes in any hand direction. In our experiments, we highlight the benefit of capturing detailed contact information when handling objects of varying properties. Interestingly, we found rich multi-fingered tactile sensing can detect unstable grasps and provide a reactive behavior that improves the robustness of the policy. The project website can be found at https://maxyang27896.github.io/anyrotate/.

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Cited by 5 Pith papers

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

  1. NeuralTouch: Neural Descriptors for Precise Sim-to-Real Tactile Robot Control

    cs.RO 2025-10 conditional novelty 7.0

    A robot grasping system that uses neural descriptor fields to guide a tactile RL policy achieves sub-millimetre precision in simulation and zero-shot transfer to real peg-in-hole and bottle-lid-opening tasks.

  2. Towards Artificial Nerves: Biomimetic Optical-Fiber Tactile Sensing for Robots

    cs.RO 2026-07 conditional novelty 6.0

    One-to-one pin-to-optical-fiber pairing lets a soft skin send touch images to a remote camera, where image moments localize contacts to 0.4 mm and classify basic shapes.

  3. Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning

    cs.RO 2026-06 unverdicted novelty 6.0

    Real2Sim tactile calibration, layout-aware encoder pretraining, and diffusion policy aggregation from object-specific RL experts enable 27% real-world success in blind grasping on a LEAP Hand for 10 seen and 10 unseen...

  4. PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation

    cs.RO 2026-03 unverdicted novelty 6.0

    PTLD distills real privileged tactile data into a state estimator to boost sim-to-real performance of proprioceptive dexterous manipulation policies, yielding 182% improvement on in-hand rotation and 57% on reorientat...

  5. PTLD: Sim-to-real Privileged Tactile Latent Distillation for Dexterous Manipulation

    cs.RO 2026-03 conditional novelty 6.0

    A sim-to-real method that distills a privileged camera-based teacher policy into a tactile student policy, improving in-hand rotation and reorientation over proprioception-only policies.