AI RESEARCH
CLANE: Continual Learning of Actions on Neuromorphic Hardware from Event Cameras
arXiv CS.AI
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ArXi:2605.28387v1 Announce Type: cross Recognizing and continuously learning novel human actions without forgetting prior classes is a requirement for emerging AR/VR and robotics applications. For these applications, both on-device processing and learning are essential for privacy and low-latency adaptation. Event cameras address the efficiency of visual sensing with sparse, asynchronous output that is naturally compatible with neuromorphic processing. Yet no prior system has deployed a continual on-device learning pipeline for event-based action recognition using neuromorphic hardware.