REACT: A Fully Spiking State-Space Model for Real-Time Event-Driven Temporal Perception
为解决机器人系统实时感知问题，提出REACT模型，该模型基于全脉冲状态空间处理事件流，无需时间累积，实现低延迟的连续时间感知。
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Biomechanically consistent real-time action recognition for human-robot interaction
This work addresses the challenge of real-time, biomechanically plausible human action recognition in industrial settings using standard 2D cameras. We propose an end-to-end framework that takes joint angles—not joint coordinates—as input, integrating human kinematic modeling and biomechanical priors into a lightweight Transformer architecture equipped with a temporal smoothing mechanism. This design significantly enhances robustness against pose variations, inter-subject anatomical differences, and camera viewpoint shifts, enabling truly low-latency online interaction. Evaluated on a custom industrial action dataset comprising 11 subjects, our method achieves 88% classification accuracy, outperforming mainstream real-time baselines. Furthermore, it successfully enables real-time closed-loop control of a simulated robot, demonstrating practical applicability in industrial automation scenarios.
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REACT: A Fully Spiking State-Space Model for Real-Time Event-Driven Temporal Perception
为解决机器人系统实时感知问题，提出REACT模型，该模型基于全脉冲状态空间处理事件流，无需时间累积，实现低延迟的连续时间感知。
0 citationsRead paper
Biomechanically consistent real-time action recognition for human-robot interaction
This work addresses the challenge of real-time, biomechanically plausible human action recognition in industrial settings using standard 2D cameras. We propose an end-to-end framework that takes joint angles—not joint coordinates—as input, integrating human kinematic modeling and biomechanical priors into a lightweight Transformer architecture equipped with a temporal smoothing mechanism. This design significantly enhances robustness against pose variations, inter-subject anatomical differences, and camera viewpoint shifts, enabling truly low-latency online interaction. Evaluated on a custom industrial action dataset comprising 11 subjects, our method achieves 88% classification accuracy, outperforming mainstream real-time baselines. Furthermore, it successfully enables real-time closed-loop control of a simulated robot, demonstrating practical applicability in industrial automation scenarios.
0 citationsRead paper