Static and Dynamic Representations for Tactile Contact-Angle Estimation with Event-Based Sensors

2026-06-02

AI Summary

This work proposes a low-latency, high-accuracy method for contact angle estimation using the event-based tactile sensor NeuroTac to meet the demands of real-time tactile perception in complex robotic interactions. By constructing and systematically evaluating static, dynamic, and fused spatial contour representations, the study demonstrates that the static representation achieves significantly superior accuracy and robustness. The approach attains mean absolute errors of 0.160° and 0.251° during continuous rolling and stationary phases, respectively, and exhibits strong adaptability to variations in sliding velocity and indentation depth. Furthermore, the end-to-end processing latency remains below 10 ms at the 99th percentile (P99), making it well-suited for high-speed robotic manipulation under varying operational conditions.

Abstract

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.

Problem

• contact-angle estimation
• event-based tactile sensing
• tactile representation
• robotic manipulation
• NeuroTac

Innovation

• event-based tactile sensing
• contact-angle estimation
• static representation
• dynamic representation
• low-latency processing