用声音和视觉结合检测机器人抓握时的外部接触点。

Robust manipulation often hinges on a robot's ability to perceive extrinsic contacts-contacts between a grasped object and its surrounding environment. However, these contacts are difficult to observe through vision alone due to occlusions, limited resolution, and ambiguous near-contact states. In this paper, we propose a visual-auditory method for extrinsic contact estimation that integrates global scene information from vision with local contact cues obtained through active audio sensing. Our approach equips a robotic gripper with contact microphones and conduction speakers, enabling the system to emit and receive acoustic signals through the grasped object to detect external contacts. We train our perception pipeline entirely in simulation and zero-shot transfer to the real world. To bridge the sim-to-real gap, we introduce a real-to-sim audio hallucination technique, injecting real-world audio samples into simulated scenes with ground-truth contact labels. The resulting multimodal model accurately estimates both the location and size of extrinsic contacts across a range of cluttered and occluded scenarios. We further demonstrate that explicit contact prediction significantly improves policy learning for downstream contact-rich manipulation tasks.