Bridging the Morphology Gap: Adapting VLA Models to Dexterous Manipulation via Intent-Conditioned Fine-Tuning
arXiv:2606.12109v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable zero-shot generalization in robotic manipulation, yet the vast majority of pre-trained pipelines remain strictly confined to low-DoF parallel grippers. Adapting these rich semantic priors to high-DoF dexterous hands introduces a severe morphology gap, direct end-to-end joint fine-tuning inherently causes catastrophic forgetting of spatial reasoning and acute action manifold collapse due to data scarcity. In this paper, we present InDex, a novel, data-efficient adaptation framework
The proliferation of Vision-Language-Action (VLA) models in robotics is prompting research into more sophisticated adaptation techniques for dexterous manipulation.
This research addresses a critical limitation in deploying advanced AI models to high-DoF robotic systems, which is essential for general-purpose robotic applications.
The proposed InDex framework could enable more efficient and robust adaptation of VLA models to complex dexterous robotic hands, overcoming current 'morphology gaps' and data scarcity issues.
- · Robotics companies
- · AI research labs
- · Manufacturing sector
- · Tasks requiring manual dexterity
Improved dexterity in robotic manipulation will accelerate the development of more capable and versatile robots.
Enhanced robotic capabilities could lead to automation of a wider range of complex tasks in industry and potentially domestic settings.
The increased practical application of dexterous robots may drive down costs and foster entirely new service industries.
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Read at arXiv cs.AI