用事件传感器估算触觉接触角，静态表征更准更稳。
Static and Dynamic Representations for Tactile Contact-Angle Estimation with Event-Based Sensors
- 用事件流构建静态、动态及混合表征，对比其触觉角度估计效果。
- 静态表征在连续滚动和中断运动中平均误差仅0.160°和0.251°。
- 静态表征对速度与压深变化更鲁棒，适合高频率机器人操作场景。
事件式触觉传感可在密集接触的机器人交互中实现低延迟信号采集。本文研究基于事件式触觉传感器（NeuroTac）事件流的接触角估计，对比三种事件衍生的空间轮廓表征：动态表征捕捉近期事件活动，静态表征恢复更持久的接触状态，以及两者的结合表征。在所有测试运动场景中，各表征管道在全部采样间隔下均实现低于10 ms的P99处理延迟，证明其在高频事件式触觉角度估计中的潜力。在特定场景训练下，静态表征表现最优，连续滚动时平均绝对误差（MAE）为0.160°，随机中断运动时停止阶段平均MAE为0.251°；且在速度与压深变化下的性能波动更小。
原文摘要 · Abstract (English)
Event-based tactile sensing offers low-latency signal acquisition for contact-rich robotic interaction. This paper investigates contact-angle estimation using event streams from an event-based tactile sensor (NeuroTac) and compares three event-derived spatial contour representations: a dynamic representation capturing recent event activity, a static representation recovering a more persistent contact state, and their combined representation. Across the evaluated motion scenarios, all representation pipelines exhibited P99 processing latency below 10 ms at all tested sampling intervals, demonstrating their potential for high-frequency event-based tactile angle estimation in robotic manipulation. The static representation consistently achieved marginally better performance than the dynamic and combined representations under scenario-specific training, yielding a mean overall MAE of 0.160° during continuous sensor rolling and a stop-phase mean MAE of 0.251° during randomly inserted motion interruptions. It also exhibited smaller performance fluctuations across speed and indentation depth variations than the other two representations.
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