FD-VLA: Force-Distilled Vision-Language-Action Model for Contact-Rich Manipulation

• URL: http://arxiv.org/abs/2602.02142v1
• Date: Mon, 02 Feb 2026 14:19:46 GMT
• Title: FD-VLA: Force-Distilled Vision-Language-Action Model for Contact-Rich Manipulation
• Authors: Ruiteng Zhao, Wenshuo Wang, Yicheng Ma, Xiaocong Li, Francis E. H. Tay, Marcelo H. Ang, Haiyue Zhu,
• Abstract summary: We present Force-Distilled VLA, a novel framework that integrates force awareness into contact-rich manipulation.<n>The core of our approach is a Force Distillation Module (FDM), which distills force by mapping a learnable query token.<n>During inference, this distilled force token is injected into the pretrained VLM, enabling force-aware reasoning.
• Score: 8.726448573057725
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Force sensing is a crucial modality for Vision-Language-Action (VLA) frameworks, as it enables fine-grained perception and dexterous manipulation in contact-rich tasks. We present Force-Distilled VLA (FD-VLA), a novel framework that integrates force awareness into contact-rich manipulation without relying on physical force sensors. The core of our approach is a Force Distillation Module (FDM), which distills force by mapping a learnable query token, conditioned on visual observations and robot states, into a predicted force token aligned with the latent representation of actual force signals. During inference, this distilled force token is injected into the pretrained VLM, enabling force-aware reasoning while preserving the integrity of its vision-language semantics. This design provides two key benefits: first, it allows practical deployment across a wide range of robots that lack expensive or fragile force-torque sensors, thereby reducing hardware cost and complexity; second, the FDM introduces an additional force-vision-state fusion prior to the VLM, which improves cross-modal alignment and enhances perception-action robustness in contact-rich scenarios. Surprisingly, our physical experiments show that the distilled force token outperforms direct sensor force measurements as well as other baselines, which highlights the effectiveness of this force-distilled VLA approach.

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