Bridging the Morphology Gap: Adapting VLA Models to Dexterous Manipulation via Intent-Conditioned Fine-Tuning

Researchers introduce InDex, a framework that adapts Vision-Language-Action (VLA) models from simple parallel grippers to complex dexterous robotic hands through intent-conditioned fine-tuning. The approach uses a two-stage architecture that preserves spatial reasoning capabilities while efficiently learning fine-grained multi-finger control with minimal training data.

InDex addresses a fundamental challenge in robotics: transferring pre-trained AI models across different mechanical morphologies. Most VLA models excel at controlling basic parallel grippers but struggle with dexterous hands due to the massive jump in degrees of freedom. The morphology gap problem has plagued robotics research because retraining from scratch causes catastrophic forgetting—the model loses its learned spatial understanding—while direct fine-tuning on limited dexterous data produces collapsed action representations.

The framework's innovation lies in treating the parallel gripper's grasp output as a semantic intent proxy rather than discarding it entirely. This continuous grasp signal guides a decoupled two-stage learning process: first aligning the visual-language backbone to predict arm trajectories and grasp intent, then using a frozen spatial backbone with a diffusion head for precise finger control. This architecture respects the hierarchical nature of manipulation—gross arm movement informs fine-grained finger articulation—mirroring how human dexterous control operates.

For the robotics industry, InDex demonstrates significant practical value. Data efficiency matters enormously in robotics because collecting dexterous manipulation demonstrations remains expensive and time-consuming. The framework's ability to leverage pre-trained models while working with minimal additional data accelerates development timelines for complex manipulation tasks. This approach could enable broader adoption of dexterous systems in manufacturing, assembly, and precision handling applications where parallel grippers prove inadequate.

Looking forward, the methodology suggests a path toward generalized cross-morphology adaptation. Future work likely explores applying this semantic inheritance approach across other gripper types and morphologies, potentially creating a more universal foundation for robotic control systems.

• →InDex bridges the morphology gap by repurposing parallel gripper outputs as intent signals for dexterous hand control
• →Two-stage decoupled architecture preserves spatial reasoning while enabling efficient fine-grained manipulation learning
• →Framework requires minimal demonstration data while outperforming end-to-end fine-tuning baselines
• →Semantic inheritance approach maintains robust generalization capabilities from original pre-trained VLA models
• →Methodology demonstrates practical pathway for adapting visual-language models across different robotic morphologies