Acoustic Sensing for Universal Jamming Grippers

2026-02-27

AI Summary

This work proposes a non-invasive acoustic sensing approach that preserves the inherent compliance of jamming-based grippers, which is typically compromised by conventional tactile sensors. By repurposing the soft body of the gripper itself as a sensor—using an embedded speaker to emit sound waves and a microphone to capture object-modulated signals—the method enables high-fidelity perception without structural intrusion. Coupled with machine learning for signal decoding, the system achieves precise estimation of multiple object attributes: object size with a mean error of only 2.6 mm, orientation within 0.6°, perfect (100%) material classification accuracy, and 85.6% accuracy in recognizing 16 everyday objects. Furthermore, the approach demonstrates robust performance over an extended 53-minute continuous grasping task, confirming its reliability and practicality for real-world applications.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Multimodal Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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.

Problem

• acoustic sensing
• universal jamming grippers
• tactile sensing
• compliance preservation
• object perception

Innovation

• acoustic sensing
• universal jamming gripper
• morphological sensing
• disentangled representation
• tactile perception