title: DexGraspVLA: A Vision-Language-Action Framework Towards General
    Dexterous Grasping
summary: Dexterous grasping remains a fundamental yet challenging problem in robotics.
  A general-purpose robot must be capable of grasping diverse objects in
  arbitrary scenarios. However, existing research typically relies on specific
  assumptions, such as single-object settings or limited environments, leading to
  constrained generalization. Our solution is DexGraspVLA, a hierarchical
  framework that utilizes a pre-trained Vision-Language model as the high-level
  task planner and learns a diffusion-based policy as the low-level Action
  controller. The key insight lies in iteratively transforming diverse language
  and visual inputs into domain-invariant representations, where imitation
  learning can be effectively applied due to the alleviation of domain shift.
  Thus, it enables robust generalization across a wide range of real-world
  scenarios. Notably, our method achieves a 90+% success rate under thousands of
  unseen object, lighting, and background combinations in a ``zero-shot''
  environment. Empirical analysis further confirms the consistency of internal
  model behavior across environmental variations, thereby validating our design
  and explaining its generalization performance. We hope our work can be a step
  forward in achieving general dexterous grasping. Our demo and code can be found
  at https://dexgraspvla.github.io/.
paper_authors._id: 67c5beea1b2c18e03a3d5218
paper_authors.name: Yifan Zhong
paper_authors._id: 67c5beea1b2c18e03a3d5219
paper_authors.name: Xuchuan Huang
paper_authors._id: 67c5beea1b2c18e03a3d521a
paper_authors.name: Ruochong Li
paper_authors._id: 67c5beea1b2c18e03a3d521b
paper_authors.name: Ceyao Zhang
paper_authors._id: 67c5beea1b2c18e03a3d521c
paper_authors.name: Yitao Liang
paper_authors._id: 67c5beea1b2c18e03a3d521d
paper_authors.name: Yaodong Yang
paper_authors._id: 67c5beea1b2c18e03a3d521e
paper_authors.name: Yuanpei Chen
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-03-03T14:38:37.342Z
paper_authors._id: 6393a8af84c565d2c3419b7c
paper_authors.avatarUrl: /avatars/f2a237a58dd0a25ef5c1a98e60acbb5c.svg
paper_authors.fullname: chen
paper_authors.type: user
paper_authors.user: yuanpei
paper_title: DexGraspVLA: A Vision-Language-Action Framework Towards General
    Dexterous Grasping
paper_summary: Dexterous grasping remains a fundamental yet challenging problem in robotics.
  A general-purpose robot must be capable of grasping diverse objects in
  arbitrary scenarios. However, existing research typically relies on specific
  assumptions, such as single-object settings or limited environments, leading to
  constrained generalization. Our solution is DexGraspVLA, a hierarchical
  framework that utilizes a pre-trained Vision-Language model as the high-level
  task planner and learns a diffusion-based policy as the low-level Action
  controller. The key insight lies in iteratively transforming diverse language
  and visual inputs into domain-invariant representations, where imitation
  learning can be effectively applied due to the alleviation of domain shift.
  Thus, it enables robust generalization across a wide range of real-world
  scenarios. Notably, our method achieves a 90+% success rate under thousands of
  unseen object, lighting, and background combinations in a ``zero-shot''
  environment. Empirical analysis further confirms the consistency of internal
  model behavior across environmental variations, thereby validating our design
  and explaining its generalization performance. We hope our work can be a step
  forward in achieving general dexterous grasping. Our demo and code can be found
  at https://dexgraspvla.github.io/.
paper_ai_keywords: Vision-Language model diffusion-based policy hierarchical framework domain-invariant representations imitation learning zero-shot environment