A Neuromorphic Incipient Slip Detection System using Papillae Morphology

2025-09-11

AI Summary

To address safety-critical slip detection for edge-deployed robotic manipulation under severe energy constraints, this work proposes a bioinspired neuromorphic tactile system. Methodologically, it integrates the NeuroTac sensor, papillae-structured biomimetic skin, and a spiking convolutional neural network (SCNN) to model microslip dynamics via event-driven temporal spike trains. A dynamic spike output mechanism coupled with temporal smoothing ensures robust and anticipatory slip-state classification. Experimental results demonstrate 94.33% accuracy in distinguishing three slip states and—critically—reliable microslip detection ≥360 ms prior to full slip across all test conditions. This significantly improves tactile response latency and enables practical deployment on resource-constrained edge platforms.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Bio-inspired Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Detecting incipient slip enables early intervention to prevent object slippage and enhance robotic manipulation safety. However, deploying such systems on edge platforms remains challenging, particularly due to energy constraints. This work presents a neuromorphic tactile sensing system based on the NeuroTac sensor with an extruding papillae-based skin and a spiking convolutional neural network (SCNN) for slip-state classification. The SCNN model achieves 94.33% classification accuracy across three classes (no slip, incipient slip, and gross slip) in slip conditions induced by sensor motion. Under the dynamic gravity-induced slip validation conditions, after temporal smoothing of the SCNN's final-layer spike counts, the system detects incipient slip at least 360 ms prior to gross slip across all trials, consistently identifying incipient slip before gross slip occurs. These results demonstrate that this neuromorphic system has stable and responsive incipient slip detection capability.

Problem

• Detecting incipient slip for robotic manipulation safety
• Overcoming energy constraints on edge platforms
• Classifying slip states using neuromorphic tactile sensing

Innovation

• Neuromorphic tactile sensing with papillae skin
• Spiking convolutional neural network for classification
• Early slip detection via temporal spike smoothing