A-SLIP: Acoustic Sensing for Continuous In-hand Slip Estimation
2026-04-09 • Robotics
Robotics
AI summaryⓘ
The authors developed A-SLIP, a system that uses tiny microphones behind a soft pad on a robot gripper to listen to vibrations when holding objects. Their method helps the robot figure out if an object is slipping, how fast, and in which direction, better than existing sensors. They use sound patterns processed by a simple neural network to make these slip predictions in real time. Tests show their multi-microphone design is much more accurate than single microphones or other methods, helping robots grab objects more reliably.
in-hand manipulationslip detectionpiezoelectric microphonesparallel-jaw gripperlog-mel spectrogramconvolutional neural networktactile sensingrobotic graspingvibration sensingreal-time estimation
Authors
Uksang Yoo, Yuemin Mao, Jean Oh, Jeffrey Ichnowski
Abstract
Reliable in-hand manipulation requires accurate real-time estimation of slip between a gripper and a grasped object. Existing tactile sensing approaches based on vision, capacitance, or force-torque measurements face fundamental trade-offs in form factor, durability, and their ability to jointly estimate slip direction and magnitude. We present A-SLIP, a multi-channel acoustic sensing system integrated into a parallel-jaw gripper for estimating continuous slip in the grasp plane. The A-SLIP sensor consists of piezoelectric microphones positioned behind a textured silicone contact pad to capture structured contact-induced vibrations. The A-SLIP model processes synchronized multi-channel audio as log-mel spectrograms using a lightweight convolutional network, jointly predicting the presence, direction, and magnitude of slip. Across experiments with robot- and externally induced slip conditions, the fine-tuned four-microphone configuration achieves a mean absolute directional error of 14.1 degrees, outperforms baselines by up to 12 percent in detection accuracy, and reduces directional error by 32 percent. Compared with single-microphone configurations, the multi-channel design reduces directional error by 64 percent and magnitude error by 68 percent, underscoring the importance of spatial acoustic sensing in resolving slip direction ambiguity. We further evaluate A-SLIP in closed-loop reactive control and find that it enables reliable, low-cost, real-time estimation of in-hand slip. Project videos and additional details are available at https://a-slip.github.io.