FineCraft: Enhancing VLA Models for Fine-Grained Manipulation
Overview
This research presents FineCraft, a framework designed to enhance Vision-Language-Action (VLA) models for fine-grained manipulation tasks. By utilizing a task-routed mixture of policies that selects between independently adapted DINOv2-DiT or pretrained pi_0.5 policies, the system achieves an overall score of 0.878 in the MetaFine Challenge, ranking fourth and exceeding reference performance on all five tasks. The approach integrates local visual features for object part identification, expert demonstrations for action teaching, and replanning guided by new observations. Official results and local diagnostics characterize performance under visual perturbations, instruction changes, and multi-stage manipulation scenarios.
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“ges, and multi-stage manipulation. Code is available at https://github.com/eqcy/FineCraft”
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- arXiv Robotics — research abstractsSignalFineCraft: Enhancing VLA Models for Fine-Grained Manipulation
This research presents FineCraft, a framework designed to enhance Vision-Language-Action (VLA) models for fine-grained manipulation tasks. By utilizing a task-routed mixture of policies that selects between independently adapted DINOv2-DiT or pretrained pi_0.5 policies, the system achieves an overall score of 0.878 in the MetaFine Challenge, ranking fourth and exceeding reference performance on all five tasks. The approach integrates local visual features for object part identification, expert demonstrations for action teaching, and replanning guided by new observations. Official results and local diagnostics characterize performance under visual perturbations, instruction changes, and multi-stage manipulation scenarios.
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