Bridging the Morphology Gap: Adapting VLA Models to Dexterous Manipulation via Intent-Conditioned Fine-Tuning
Overview
This research introduces InDex, a framework that adapts pretrained vision-language-action (VLA) models for dexterous manipulation by explicitly conditioning on grasp intent.
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Event evidence and corrections
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Environment: simulation
“control with low-rate co-adaptation. Across four simulated tasks, InDex with $\pi_{0.5}$ achieves 85.8% mean task success, improving the native policy by 35.5 percentage points, and reaches 69.0% on held-out objects with similar geometry without fine-tuning or recalibration. Improvements also hold across three VLA backbones and separately trained real-world policies, where mean success increases from 71.0% to 83.0%.”
Exact source · revision 1Source owner not reported
Reported quantity · number of simulated tasks: 4 other · Basis not reported
“control with low-rate co-adaptation. Across four simulated tasks, InDex with $\pi_{0.5}$ achieves 85.8% mean task success, improving the native policy by 35.5 percentage points, and reaches 69.0% on held-out objects with similar geometry without fine-tuning or recalibration. Improvements also hold across three VLA backbones and separately trained real-world policies, where mean success increases from 71.0% to 83.0%.”
Exact source · revision 1Source owner not reported
Reported quantity · number of VLA backbones: 3 other · Basis not reported
“control with low-rate co-adaptation. Across four simulated tasks, InDex with $\pi_{0.5}$ achieves 85.8% mean task success, improving the native policy by 35.5 percentage points, and reaches 69.0% on held-out objects with similar geometry without fine-tuning or recalibration. Improvements also hold across three VLA backbones and separately trained real-world policies, where mean success increases from 71.0% to 83.0%.”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalBridging the Morphology Gap: Adapting VLA Models to Dexterous Manipulation via Intent-Conditioned Fine-Tuning
This research introduces InDex, a framework that adapts pretrained vision-language-action (VLA) models for dexterous manipulation by explicitly conditioning on grasp intent.
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