Continual Learning for Autonomous Robots: A Prototype-based Approach

arXiv:2404.00418v1 Announce Type: new
Abstract: Humans and animals learn throughout their lives from limited amounts of sensed data, both with and without supervision. Autonomous, intelligent robots of the future are often expected to do the same. The existing continual learning (CL) methods are usually not directly applicable to robotic settings: they typically require buffering and a balanced replay of training data. A few-shot online continual learning (FS-OCL) setting has been proposed to address more realistic scenarios where robots must learn …

abstract animals arxiv autonomous autonomous robots continual cs.cv cs.lg cs.ro data future humans intelligent learn robotic robots supervision type