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.
翻译:可靠的手内操作要求实时准确估计夹爪与被抓物体之间的滑移。现有的基于视觉、电容或力-扭矩测量的触觉传感方法在形态因子、耐久性以及联合估计滑移方向与幅值的能力方面存在根本性权衡。我们提出A-SLIP——一种集成于平行夹爪中的多通道声学传感系统,用于估计抓取平面内的连续滑移。A-SLIP传感器由位于纹理化硅胶接触垫后方的压电麦克风组成,用于捕获结构化的接触诱发振动。A-SLIP模型利用轻量级卷积网络将同步的多通道音频处理为对数梅尔频谱图,联合预测滑移的存在性、方向与幅值。在机器人诱发与外部诱发滑移条件下的实验中,经过微调的四麦克风配置实现了14.1度的平均绝对方向误差,检测精度相比基线方法提升高达12%,方向误差降低32%。与单麦克风配置相比,多通道设计将方向误差降低64%,幅值误差降低68%,凸显了空间声学传感在解决滑移方向歧义性问题中的重要性。我们进一步在闭环反应式控制中评估A-SLIP,发现其能够实现可靠、低成本、实时的滑移估计。项目视频及更多详情可访问https://a-slip.github.io。