{"ID":5936993,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T15:38:11.834581458Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05241","arxiv_id":"2607.05241","title":"GelNeuro: A Sensing-Computing Integrated Neuromorphic Tactile System for Texture Recognition","abstract":"Neuromorphic visuo-tactile sensing offers a promising paradigm for low-latency and low-power robotic perception. However, existing systems still rely heavily on a host computer for event readout, preprocessing, or relaying prior to chip inference. This paper presents GelNeuro, a fully integrated sensing-computing visuo-tactile system that directly pairs a GelSight Mini-based optical tactile front end with the Speck2f neuromorphic system-on-chip (SoC). Contact-induced marker motions are captured as dynamic vision sensor (DVS) events and routed through the on-chip network to a spiking convolutional neural network (SCNN) classifier. To mitigate accuracy degradation during 8-bit deployment, a hardware-aware weight clamping strategy is introduced. Evaluated on a 15-class natural texture recognition task, hardware-in-the-loop testing on the physical chip achieves a 96.3% accuracy within an 80 ms inference window. Notably, the system consumes only 19.6 mW of board-level active power-over three orders of magnitude lower than conventional CPU/GPU baselines on the same benchmark. GelNeuro also exhibits robust generalization across unseen contact depths, demonstrating the viability of direct sensor-to-chip tactile recognition on edge neuromorphic hardware.","short_abstract":"Neuromorphic visuo-tactile sensing offers a promising paradigm for low-latency and low-power robotic perception. However, existing systems still rely heavily on a host computer for event readout, preprocessing, or relaying prior to chip inference. This paper presents GelNeuro, a fully integrated sensing-computing visuo...","url_abs":"https://arxiv.org/abs/2607.05241","url_pdf":"https://arxiv.org/pdf/2607.05241v1","authors":"[\"Luoyang Bian\",\"Xinpan Meng\",\"Zhenghua Ma\",\"Houcheng Li\",\"Long Cheng\"]","published":"2026-07-06T15:52:35Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}