← 研踪 PaperTrailarXiv:2606.12109 · v3
Bridging the Morphology Gap: Adapting VLA Models to Dexterous Manipulation via Intent-Conditioned Fine-Tuning
首次提交 2026-06-10 22:03
版本更新 2026-11-04 15:20
雷达观测 2026-11-05 11:10
摘要
Pretrained vision-language-action (VLA) models offer semantic and spatial priors for robot manipulation, but their parallel-gripper interfaces leave multi-finger contact coordination implicit. Adapting these models requires both timely grasp transitions and embodiment-specific hand trajectories. We introduce InDex, an intent-conditioned framework that connects these two requirements. A normalized aperture signal, derived from retargeted demonstrations, supervises a scalar grasp intent while preserving full hand-action supervision. Stage 1 learns synchronized end-effector-intent chunks; Stage 2 conditions a diffusion hand decoder on these predictions, visual-language context, and proprioception. Sequential optimization establishes the upstream interface before learning dexterous control with low-rate co-adaptation. Across four simulated tasks, InDex with $π_{0.5}$ 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%. Component ablations and timing interventions support the value of explicit grasp-intent conditioning and expressive hand generation. These results identify arm-hand coordination as an actionable bottleneck in adapting parallel-gripper priors to dexterous manipulation.
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v3 · 2026-11-04 15:20 · 最新观测版本
Bridging the Morphology Gap: Adapting VLA Models to Dexterous Manipulation via Intent-Conditioned Fine-Tuning
Pretrained vision-language-action (VLA) models offer semantic and spatial priors for robot manipulation, but their parallel-gripper interfaces leave multi-finger contact coordination implicit. Adapting these models requires both timely grasp transitions and embodiment-specific hand trajectories. We introduce InDex, an intent-conditioned framework that connects these two requirements. A normalized aperture signal, derived from retargeted demonstrations, supervises a scalar grasp intent while preserving full hand-action supervision. Stage 1 learns synchronized end-effector-intent chunks; Stage 2 conditions a diffusion hand decoder on these predictions, visual-language context, and proprioception. Sequential optimization establishes the upstream interface before learning dexterous control with low-rate co-adaptation. Across four simulated tasks, InDex with $π_{0.5}$ 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%. Component ablations and timing interventions support the value of explicit grasp-intent conditioning and expressive hand generation. These results identify arm-hand coordination as an actionable bottleneck in adapting parallel-gripper priors to dexterous manipulation.
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