arXiv subjects

Yanhui Lu

Publications and source records attributed to Yanhui Lu.

3 recordsLinked to original sources

arXiv2026

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

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{\deg} during continuous sensor rolling and a stop-phase mean MAE of 0.251{\deg} during randomly inserted motion interruptions. It also exhibited smaller performance fluctuations across speed and indentation depth variations than the other two representations.

cs.RO↗

arXiv2025

A Neuromorphic Incipient Slip Detection System using Papillae Morphology

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.

cs.RO↗

arXiv2025

Sync-TVA: A Graph-Attention Framework for Multimodal Emotion Recognition with Cross-Modal Fusion

Multimodal emotion recognition (MER) is crucial for enabling emotionally intelligent systems that perceive and respond to human emotions. However, existing methods suffer from limited cross-modal interaction and imbalanced contributions across modalities. To address these issues, we propose Sync-TVA, an end-to-end graph-attention framework featuring modality-specific dynamic enhancement and structured cross-modal fusion. Our design incorporates a dynamic enhancement module for each modality and constructs heterogeneous cross-modal graphs to model semantic relations across text, audio, and visual features. A cross-attention fusion mechanism further aligns multimodal cues for robust emotion inference. Experiments on MELD and IEMOCAP demonstrate consistent improvements over state-of-the-art models in both accuracy and weighted F1 score, especially under class-imbalanced conditions.

cs.MM↗