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Novelty Adaptation Through Hybrid Large Language Model (LLM)-Symbolic Planning and LLM-guided Reinforcement Learning
This work proposes a neuro-symbolic architecture that integrates symbolic planning, reinforcement learning, and a large language model (LLM) to learn how to handle novel objects and outperforms the state-of-the-art methods in operator discovery as well as operator learning in continuous robotic domains.