A-SLIP：用于连续手握滑动估计的声学传感
A-SLIP: Acoustic Sensing for Continuous In-hand Slip Estimation
AI总结：
A-SLIP通过多通道声学传感系统实现手握滑动的实时连续估计，采用压电微声器捕捉接触振动，利用轻量卷积网络处理音频数据，有效提升滑动方向和幅度估计精度。
摘要：
可靠的在手操作需要准确的实时滑动估计。现有基于视觉、电容或力矩测量的触觉传感方法在形式因素、耐用性和联合估计滑动方向和幅度方面存在根本性权衡。我们提出了A-SLIP，一种集成在平行 jaw 抓取器中的多通道声学传感系统，用于估计握持平面内的连续滑动。A-SLIP传感器由压电微声器组成，位于有纹理的硅胶接触垫后方，以捕捉结构化接触引起的振动。A-SLIP模型使用轻量级卷积网络处理同步的多通道音频，作为log-mel频谱图，联合预测滑动的存在、方向和幅度。在机器人和外部诱导滑动条件的实验中，经过微调的四麦克风配置实现了平均方向误差为14.1度，比基线提高了12%的检测准确性，方向误差减少了32%。与单麦克风配置相比，多通道设计将方向误差减少了64%，幅度误差减少了68%，凸显了空间声学传感在解决滑动方向歧义中的重要性。我们进一步评估了A-SLIP在闭环反应控制中的性能，发现其能够实现可靠、低成本、实时的在手滑动估计。项目视频和额外细节可在https://a-slip.github.io获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
