Acoustic Sensing for Universal Jamming Grippers

Abstract

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.

Figures (15)

• Figure 1: Left: Universal jamming grippers conform to unknown objects, enabling versatile grasping, but traditional sensors restrict this deformability. Right: We address this with acoustic sensing. A speaker emits sound into the gripper cavity, which propagates through the gripper and object, encoding object properties (size, material, orientation, class, etc.) in the modulated signal shown in green. A microphone then records the signal, and machine learning reconstructs the object state.
• Figure 2: Different granular media inside the gripper have different sound absorption characteristics. Good sound reflection is necessary to return information about the environment to the microphone. The plot shows the energy of the sound signal as reflected by different granular media inside the gripper. Results are scaled relative to the sound reflection in an empty gripper (i.e., when only air is inside). We use plastic ball bearings as our granular medium because they better reflect the speaker's sound than ground coffee and metal ball bearings.
• Figure 3: The sensing and grasping process. We sense after conforming to the object, when the contact area for acoustic energy exchange is large, but before jamming, because the lower air pressure during evacuation reduces sound transmission in the cavity and the vacuum pump generates substantial noise. The supplementary video shows the grasping process.
• Figure 4: The object set used in this paper (bottom: cubes of different sizes for Section \ref{['sec:sizes']}; left: plates and spheres of different materials for Section \ref{['sec:material']}; right: everyday objects from the YCB ycb dataset for Section \ref{['sec:obj_class']})
• Figure 5: The acoustic sensor has high spatial resolution: it can accurately predict the size of enclosed objects. Our model predicts the cube edge length with a small root mean square error (RMSE) of 2.7mm for known objects, 2.4mm for unknown objects, and an overall error of 2.6mm. Fig. \ref{['fig:objects']} shows the corresponding cubes.