Robotics

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Paper summary

Combines LLM-based planning and perception with few-shot summarization to infer user preferences.

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Key points

Preference inference: Uses LLMs to infer generalized user preferences from a few examples of what objects belong where in a home.

Generalization: Preferences inferred from specific examples generalize to future unseen objects.

LLMs in embodied AI: Demonstrates LLMs' value for household robotics as high-level preference reasoners.

Personalized robots: An early example of LLM-powered robot personalization - informing 2024 agent+robotics research.

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