March 13, 2024, 4:43 a.m. | Byeonghwi Kim, Minhyuk Seo, Jonghyun Choi

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.07548v1 Announce Type: cross
Abstract: In learning an embodied agent executing daily tasks via language directives, the literature largely assumes that the agent learns all training data at the beginning. We argue that such a learning scenario is less realistic since a robotic agent is supposed to learn the world continuously as it explores and perceives it. To take a step towards a more realistic embodied agent learning scenario, we propose two continual learning setups for embodied agents; learning new …

agents arxiv continual cs.ai cs.lg cs.ro interactive type

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