An event-based opto-tactile skin

2026-01-07

Frontiers in Neuroscience

AI Summary

This work addresses the limitations of conventional large-area flexible tactile sensors—namely high power consumption, response latency, and data redundancy—which hinder efficient tactile perception in soft robotics. The authors propose an event-driven opto-tactile skin system that uniquely integrates a dynamic vision sensor (DVS) with a flexible optical waveguide. By leveraging dual-view triangulation and DBSCAN clustering, the system achieves robust pressure localization from sparse event streams. This approach drastically reduces both data volume and power consumption while maintaining high accuracy: it attains a root-mean-square error (RMSE) of 4.66 mm over a 4620 mm² sensing area. Even when event data are compressed to 1/1024 of the original, 85% of trials still yield valid localization, with the average error increasing only to 9.33 mm. The system exhibits a detection latency distribution width of 31 ms.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This paper presents a neuromorphic, event-driven tactile sensing system for soft, large-area skin, based on the Dynamic Vision Sensors (DVS) integrated with a flexible silicone optical waveguide skin. Instead of repetitively scanning embedded photoreceivers, this design uses a stereo vision setup comprising two DVS cameras looking sideways through the skin. Such a design produces events as changes in brightness are detected, and estimates press positions on the 2D skin surface through triangulation, utilizing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to find the center of mass of contact events resulting from pressing actions. The system is evaluated over a 4,620 mm probed area of the skin using a meander raster scan. Across 95 % of the presses visible to both cameras, the press localization achieved a Root-Mean-Squared Error (RMSE) of 4.66 mm. The results highlight the potential of this approach for wide-area flexible and responsive tactile sensors in soft robotics and interactive environments. Moreover, we examined how the system performs when the amount of event data is strongly reduced. Using stochastic down-sampling, the event stream was reduced to 1/1,024 of its original size. Under this extreme reduction, the average localization error increased only slightly (from 4.66 mm to 9.33 mm), and the system still produced valid press localizations for 85 % of the trials. This reduction in pass rate is expected, as some presses no longer produce enough events to form a reliable cluster for triangulation. These results show that the sensing approach remains functional even with very sparse event data, which is promising for reducing power consumption and computational load in future implementations. The system exhibits a detection latency distribution with a characteristic width of 31 ms.

Problem

• tactile sensing
• event-based sensing
• soft robotics
• press localization
• neuromorphic vision

Innovation

• event-based sensing
• opto-tactile skin
• neuromorphic vision
• DBSCAN clustering
• sparse event data