用声音感知代替触觉，让软体夹爪不牺牲抓取性能也能识别物体。
Acoustic Sensing for Universal Jamming Grippers
- 利用内部扬声器麦克风通过声音变化感知物体特性。
- 识别物体大小误差仅2.6毫米，方向误差0.6度，材料识别准确率100%。
- 适合需要高鲁棒性与长期稳定工作的机器人抓取任务。
通用气动吸附夹爪因其柔性结构能有效抓取未知物体，但传统触觉传感器会破坏其柔韧性，降低抓取性能。本文提出一种基于声学的形态感知方法，将夹爪柔软本体作为传感器：在腔体内放置扬声器与麦克风，远离可变形膜，完全保持柔性。声音通过夹爪与物体传播，编码物体属性，再由机器学习重建。该传感器实现高空间分辨率，物体尺寸识别误差为2.6毫米，方向识别误差为0.6度；在80 dBA外部噪声下仍具鲁棒性；材料识别准确率达100%，可区分16种日常物体，准确率为85.6%。我们在真实触觉分拣任务中验证，连续运行53分钟无中断，证实抓取性能未受影响。最后，我们展示了可解耦的声学表征学习，提升了对无关声学变化的鲁棒性。
原文摘要 · Abstract (English)
Universal jamming grippers excel at grasping unknown objects due to their compliant bodies. Traditional tactile sensors can compromise this compliance, reducing grasping performance. We present acoustic sensing as a form of morphological sensing, where the gripper's soft body itself becomes the sensor. A speaker and microphone are placed inside the gripper cavity, away from the deformable membrane, fully preserving compliance. Sound propagates through the gripper and object, encoding object properties, which are then reconstructed via machine learning. Our sensor achieves high spatial resolution in sensing object size (2.6 mm error) and orientation (0.6 deg error), remains robust to external noise levels of 80 dBA, and discriminates object materials (up to 100% accuracy) and 16 everyday objects (85.6% accuracy). We validate the sensor in a realistic tactile object sorting task, achieving 53 minutes of uninterrupted grasping and sensing, confirming the preserved grasping performance. Finally, we demonstrate that disentangled acoustic representations can be learned, improving robustness to irrelevant acoustic variations.
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