title: ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via
    Residual Learning
summary: Human hands play a central role in interacting, motivating increasing
  research in dexterous robotic manipulation. Data-driven embodied AI algorithms
  demand precise, large-scale, human-like manipulation sequences, which are
  challenging to obtain with conventional reinforcement learning or real-world
  teleoperation. To address this, we introduce ManipTrans, a novel two-stage
  method for efficiently transferring human bimanual skills to dexterous robotic
  hands in simulation. ManipTrans first pre-trains a generalist trajectory
  imitator to mimic hand motion, then fine-tunes a specific residual module under
  interaction constraints, enabling efficient learning and accurate execution of
  complex bimanual tasks. Experiments show that ManipTrans surpasses
  state-of-the-art methods in success rate, fidelity, and efficiency. Leveraging
  ManipTrans, we transfer multiple hand-object datasets to robotic hands,
  creating DexManipNet, a large-scale dataset featuring previously unexplored
  tasks like pen capping and bottle unscrewing. DexManipNet comprises 3.3K
  episodes of robotic manipulation and is easily extensible, facilitating further
  policy training for dexterous hands and enabling real-world deployments.
paper_authors._id: 67ec97f65d9c75ff46de2974
paper_authors.name: Kailin Li
paper_authors._id: 67ec97f65d9c75ff46de2975
paper_authors.name: Puhao Li
paper_authors.status: admin_assigned
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paper_authors.avatarUrl: /avatars/4b8778ed239b64de5f8781f0ba57168b.svg
paper_authors.fullname: Puhao Li
paper_authors.type: user
paper_authors.user: puhao
paper_authors._id: 67ec97f65d9c75ff46de2976
paper_authors.name: Tengyu Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-02T12:29:41.351Z
paper_authors._id: 634379a81bdd3dfa55dcbe82
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/1665366425318-noauth.png
paper_authors.fullname: Tengyu Liu
paper_authors.type: user
paper_authors.user: oxrider
paper_authors._id: 67ec97f65d9c75ff46de2977
paper_authors.name: Yuyang Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-02T12:29:48.257Z
paper_authors._id: 637cef70b8e573d75beb5748
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paper_authors.fullname: Yuyang Li
paper_authors.type: user
paper_authors.user: aidenli
paper_authors._id: 67ec97f65d9c75ff46de2978
paper_authors.name: Siyuan Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-02T12:29:56.926Z
paper_authors._id: 63c7a33121bd95f80ed74652
paper_authors.avatarUrl: /avatars/7dd59afea785a2bff0ec2b757abd474e.svg
paper_authors.fullname: Siyuan Huang
paper_authors.type: user
paper_authors.user: thuhsy
paper_title: ManipTrans: Efficient Dexterous Bimanual Manipulation Transfer via
    Residual Learning
paper_summary: Human hands play a central role in interacting, motivating increasing
  research in dexterous robotic manipulation. Data-driven embodied AI algorithms
  demand precise, large-scale, human-like manipulation sequences, which are
  challenging to obtain with conventional reinforcement learning or real-world
  teleoperation. To address this, we introduce ManipTrans, a novel two-stage
  method for efficiently transferring human bimanual skills to dexterous robotic
  hands in simulation. ManipTrans first pre-trains a generalist trajectory
  imitator to mimic hand motion, then fine-tunes a specific residual module under
  interaction constraints, enabling efficient learning and accurate execution of
  complex bimanual tasks. Experiments show that ManipTrans surpasses
  state-of-the-art methods in success rate, fidelity, and efficiency. Leveraging
  ManipTrans, we transfer multiple hand-object datasets to robotic hands,
  creating DexManipNet, a large-scale dataset featuring previously unexplored
  tasks like pen capping and bottle unscrewing. DexManipNet comprises 3.3K
  episodes of robotic manipulation and is easily extensible, facilitating further
  policy training for dexterous hands and enabling real-world deployments.
paper_ai_summary: ManipTrans is a two-stage method that efficiently transfers human bimanual skills to robotic hands in simulation, achieving high success rates and fidelity in complex tasks.
paper_ai_keywords: trajectory imitation residual module interaction constraints dexterous robotic hands bimanual tasks DexManipNet pen capping bottle unscrewing