Language-Grounded Force/Torque Pretraining for Extrinsic Contact Perception

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为 VLA、具身智能、人形机器人控制、机器人操作等高价值论文补充中文说明。

Original abstract

Fine manipulation requires robots to recognize when a grasped object makes or changes contact with its environment, yet such extrinsic contact is not always directly observable from vision or local contact geometry. Force and torque signals carry this information, but also vary with object weight, grasp configuration, and robot motion, leaving it unclear whether they can provide a reusable representation of physical state. Here we show that bilateral six-axis force/torque histories can be pretrained as a semantically structured physical representation by aligning them with a frozen language space using supervision generated from simulated pick-and-place interactions. On unseen objects in simulation, the learned representation supports text-prototype readout of robot action, object weight, and grasp interaction. In real-world placement, unlabeled calibration enables contact and support-state recognition without updating the encoder, outperforming a vertical-force sign-reversal rule baseline. For connector insertion, an event absent from pretraining, low-rank adaptation improves data-efficient learning over training from scratch. These results show that force/torque histories can support both direct readout of known physical states and efficient adaptation to new extrinsic-contact events.

Links and sources

• Official / arXiv page
• PDF from original source

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