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Holo-Dex: Teaching Dexterity with Immersive Mixed Reality

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arxiv 2210.06463 v1 pith:DKL6WPYH submitted 2022-10-12 cs.RO cs.AIcs.CVcs.HCcs.LG

classification cs.ROcs.AIcs.CVcs.HCcs.LG
keywords dexterousholo-dexskillsteachingchallengecollectdemonstrationsimmersive
verification ladder T0 review T1 audit T2 compute T3 formal
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A fundamental challenge in teaching robots is to provide an effective interface for human teachers to demonstrate useful skills to a robot. This challenge is exacerbated in dexterous manipulation, where teaching high-dimensional, contact-rich behaviors often require esoteric teleoperation tools. In this work, we present Holo-Dex, a framework for dexterous manipulation that places a teacher in an immersive mixed reality through commodity VR headsets. The high-fidelity hand pose estimator onboard the headset is used to teleoperate the robot and collect demonstrations for a variety of general-purpose dexterous tasks. Given these demonstrations, we use powerful feature learning combined with non-parametric imitation to train dexterous skills. Our experiments on six common dexterous tasks, including in-hand rotation, spinning, and bottle opening, indicate that Holo-Dex can both collect high-quality demonstration data and train skills in a matter of hours. Finally, we find that our trained skills can exhibit generalization on objects not seen in training. Videos of Holo-Dex are available at https://holo-dex.github.io.

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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. C2Dex: Contact-Consistent Reconstruction and Retargeting for Dexterous Manipulation from Monocular Video

    cs.RO 2026-08 conditional novelty 7.0 of 10

    C2Dex converts monocular human videos into executable dexterous robot manipulation trajectories by using stable object-side contacts as a shared representation for reconstruction and retargeting, achieving 57.78% and ...

  2. Play2Perfect: What Matters in Dexterous Play Pretraining for Precise Assembly?

    cs.RO 2026-06 unverdicted novelty 6.0 of 10

    Task-agnostic RL play pretraining on diverse objects yields a reusable dexterous prior that makes sparse-reward assembly learning ~33× more sample-efficient and enables zero-shot sim-to-real transfer on tight insertio...

  3. SR-Reward: Taking The Path More Traveled

    cs.LG 2025-01 conditional novelty 6.0 of 10

    SR-Reward replaces the environment reward with the L2 norm of a successor representation learned from demonstrations, enabling offline RL without reward labels.

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