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General In-Hand Object Rotation with Vision and Touch

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arxiv 2309.09979 v2 pith:IPY3CXN5 submitted 2023-09-18 cs.RO cs.AIcs.CVcs.LG

classification cs.ROcs.AIcs.CVcs.LG
keywords objectinputsmultimodalphysicalpropertiesrotationsensoryshapes
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce RotateIt, a system that enables fingertip-based object rotation along multiple axes by leveraging multimodal sensory inputs. Our system is trained in simulation, where it has access to ground-truth object shapes and physical properties. Then we distill it to operate on realistic yet noisy simulated visuotactile and proprioceptive sensory inputs. These multimodal inputs are fused via a visuotactile transformer, enabling online inference of object shapes and physical properties during deployment. We show significant performance improvements over prior methods and the importance of visual and tactile sensing.

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Forward citations

Cited by 2 Pith papers

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

  1. SpikeATac: A Multimodal Tactile Finger with Taxelized Dynamic Sensing for Dexterous Manipulation

    cs.RO 2025-10 conditional novelty 7.0 of 10

    A fingertip with 16-taxel PVDF dynamic sensing plus capacitive static sensing enables fast delicate grasping and, with RLHF fine-tuning, in-hand manipulation of fragile objects.

  2. Towards Human-level Dexterity via Robot Learning

    cs.RO 2025-07 conditional novelty 6.0 of 10

    Sampling-based planning used for reset states and pre-training makes reinforcement learning practical for dexterous in-hand manipulation of hard objects with intrinsic sensing.

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