DexGraspVLA: A Vision-Language-Action Framework Towards General
  Dexterous Grasping
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/.
67c5beea1b2c18e03a3d5218
Yifan Zhong
67c5beea1b2c18e03a3d5219
Xuchuan Huang
claimed_verified
2025-07-03T07:43:31.335Z
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XUCHUAN HUANG
user
Feernnn
67c5beea1b2c18e03a3d521a
Ruochong Li
67c5beea1b2c18e03a3d521b
Ceyao Zhang
67c5beea1b2c18e03a3d521c
Yitao Liang
claimed_verified
2025-04-09T07:38:18.891Z
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Yitao Liang
user
YitaoLiang
67c5beea1b2c18e03a3d521d
Yaodong Yang
67c5beea1b2c18e03a3d521e
Yuanpei Chen
extracted_pending
2025-03-03T14:38:37.342Z
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chen
user
yuanpei
DexGraspVLA: A Vision-Language-Action Framework Towards General
  Dexterous Grasping
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/.
A hierarchical framework using a pre-trained Vision-Language model and diffusion-based policy achieves high success rates in dexterous grasping across varied real-world scenarios.
Vision-Language model diffusion-based policy domain-invariant representations imitation learning zero-shot environment