In robotics, natural language is an accessible interface for guiding robots, potentially empowering individuals with limited training to direct behaviors, express preferences, and offer feedback. Recent studies have underscored the inherent capabilities of large language models (LLMs), pre-trained on extensive internet data, in addressing various robotics tasks. These tasks range from devising action sequences based on language commands to generating robot code. Multi-turn interactions enable real-time feedback incorporation, fostering adaptability and learning. However, the challenge lies in the LLMs’ ability to retain contextual information over prolonged interactions, leading to forgetting previous instructions…

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