CRAFT: Adapting VLA Models to Contact-rich Manipulation via Force-aware Curriculum Fine-tuning
Yike Zhang, Yaonan Wang, Xinxin Sun, Kaizhen Huang, Zhiyuan Xu, Junjie Ji, Zhengping Che, Jian Tang, Jingtao Sun

TL;DR
This paper introduces CRAFT, a curriculum fine-tuning framework that enhances vision-language-action models for contact-rich robot manipulation by integrating force signals, leading to improved success and generalization in real-world tasks.
Contribution
CRAFT is the first to incorporate a force-aware curriculum fine-tuning approach with a variational information bottleneck for VLA models in contact-rich manipulation.
Findings
CRAFT improves task success rates in real-world experiments.
The framework generalizes to unseen objects and task variations.
It effectively adapts across different VLA architectures.
Abstract
Vision-Language-Action (VLA) models have shown a strong capability in enabling robots to execute general instructions, yet they struggle with contact-rich manipulation tasks, where success requires precise alignment, stable contact maintenance, and effective handling of deformable objects. A fundamental challenge arises from the imbalance between high-entropy vision and language inputs and low-entropy but critical force signals, which often leads to over-reliance on perception and unstable control. To address this, we introduce CRAFT, a force-aware curriculum fine-tuning framework that integrates a variational information bottleneck module to regulate vision and language embeddings during early training. This curriculum strategy encourages the model to prioritize force signals initially, before progressively restoring access to the full multimodal information. To enable force-aware…
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Taxonomy
TopicsRobot Manipulation and Learning · Teleoperation and Haptic Systems · Tactile and Sensory Interactions
