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Bimanual Dexterity for Complex Tasks

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arxiv 2411.13677 v1 pith:IPDTKEF4 submitted 2024-11-20 cs.RO cs.AIcs.CVcs.LG

Bimanual Dexterity for Complex Tasks

Kenneth Shaw , Yulong Li , Jiahui Yang , Mohan Kumar Srirama , Ray Liu , Haoyu Xiong , Russell Mendonca , Deepak Pathak This is my paper
classification cs.RO cs.AIcs.CVcs.LG
keywords bimanualrobotsystemteleoperationbidexarmsdatadexterous
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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To train generalist robot policies, machine learning methods often require a substantial amount of expert human teleoperation data. An ideal robot for humans collecting data is one that closely mimics them: bimanual arms and dexterous hands. However, creating such a bimanual teleoperation system with over 50 DoF is a significant challenge. To address this, we introduce Bidex, an extremely dexterous, low-cost, low-latency and portable bimanual dexterous teleoperation system which relies on motion capture gloves and teacher arms. We compare Bidex to a Vision Pro teleoperation system and a SteamVR system and find Bidex to produce better quality data for more complex tasks at a faster rate. Additionally, we show Bidex operating a mobile bimanual robot for in the wild tasks. The robot hands (5k USD) and teleoperation system (7k USD) is readily reproducible and can be used on many robot arms including two xArms (16k USD). Website at https://bidex-teleop.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.

  1. BiDexGrasp: Coordinated Bimanual Dexterous Grasps across Object Geometries and Sizes

    cs.RO 2026-04 unverdicted novelty 7.0

    BiDexGrasp supplies a 9.7-million-grasp bimanual dexterous dataset built via two-stage synthesis and a coordinated geometry-size-adaptive model that generates grasps for unseen objects.

  2. EquiBim: Learning Symmetry-Equivariant Policy for Bimanual Manipulation

    cs.RO 2026-03 conditional novelty 6.0

    Adding a loss that enforces left-right equivariance between observations and actions improves average bimanual imitation policy success by +2.7 to +9.5 points across four observation/action settings.

  3. Scaling Cross-Embodiment World Models for Dexterous Manipulation

    cs.RO 2025-11 conditional novelty 6.0

    A single particle-based world model trained on many simulated robot hands and real human hands can plan dexterous manipulation on robot hands it never trained on.