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.

翻译：力传感是视觉-语言-动作框架的关键模态，它能够在接触密集型任务中实现精细感知与灵巧操作。本文提出力蒸馏视觉-语言-动作模型，这是一种将力感知集成到接触密集型操作中且无需依赖物理力传感器的新型框架。该方法的核心理念是力蒸馏模块，该模块通过将基于视觉观测与机器人状态的可学习查询标记映射至与实际力信号潜在表征对齐的预测力标记，从而实现力的蒸馏。在推理过程中，此蒸馏力标记被注入预训练的视觉语言模型中，使其在保持视觉-语言语义完整性的同时实现力感知推理。该设计具有两大核心优势：其一，它能够在缺乏昂贵或易损力-力矩传感器的各类机器人平台上实现实际部署，从而显著降低硬件成本与系统复杂度；其二，力蒸馏模块在视觉语言模型前引入了额外的力-视觉-状态融合先验，这增强了跨模态对齐能力，并提升了接触密集型场景下的感知-动作鲁棒性。值得注意的是，我们的物理实验表明，蒸馏力标记的性能优于直接传感器力测量及其他基线方法，这充分验证了力蒸馏视觉-语言-动作方法的有效性。