FD-VLA：接触の多いマニピュレーションのための力蒸留視覚言語行動モデル
FD-VLA: Force-Distilled Vision-Language-Action Model for Contact-Rich Manipulation
力センサを使わずに、視覚と言語から力覚を蒸留してVLAモデルに組み込み、接触を伴う器用な操作を可能にする手法を提案。
詳しい要約
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2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Ruiteng Zhao, Wenshuo Wang, Yicheng Ma, Xiaocong Li, Francis E. H. Tay, Marcelo H. Ang, Haiyue Zhu
分類: cs.RO, cs.CV
原文アブストラクト
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.
