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New Spiking Model REACT Achieves Real-Time Temporal Perception for Robots

By PulseAugur Editorial · [1 sources] ·

Researchers have developed REACT, a novel spiking state-space model designed for real-time temporal perception in robotic systems. Unlike previous methods that accumulate events into frames, REACT processes raw events individually, enabling microsecond temporal resolution and asynchronous sensing. This approach significantly reduces inference latency, allowing for faster reaction times in dynamic environments. The model has demonstrated strong performance in gesture recognition and time-to-collision estimation, outperforming existing methods in speed and energy efficiency. AI

IMPACT Enables faster, more energy-efficient temporal perception for reactive robotic systems.

RANK_REASON The item is a research paper detailing a new model and its performance on benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Spiking Model REACT Achieves Real-Time Temporal Perception for Robots

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The item is a research paper detailing a new model and its performance on benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Geoffroy Keime, Nicolas Cuperlier, Benoit R. Cottereau ·

    REACT: A Fully Spiking State-Space Model for Real-Time Event-Driven Temporal Perception

    arXiv:2609.19204v1 Announce Type: cross Abstract: Robotic systems operating in dynamic environments require visual perception that evolves continuously with the incoming sensory stream. Event cameras provide microsecond temporal resolution and asynchronous sensing, but most learn…

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