Risk-Aware Semantic Grounding for Trustworthy LLM-Based Robot Planning
From the abstract
Large language models (LLMs) are increasingly used as high-level planners in robot navigation, but their outputs may become unreliable when instructions are ambiguous, unsupported by the environment, or semantically inconsistent. This paper presents a Risk-Aware Semantic Grounding framework for trustworthy LLM-based robot planning.
From the abstract. Our summary is in progress.