LATIDIA · Robótica

Fundamentación semántica sensible al riesgo para una planificación de robots confiable basada en LLM

arXiv: 2609.37554v1Tipo de anuncio: nuevo Resumen: Los modelos de lenguaje grandes (LLM) se utilizan cada vez más como planificadores de alto nivel en la navegación de robots, pero sus resultados pueden volverse poco confiables cuando las instrucciones son ambiguas, inseguras

La noticia

arXiv:2609.37554v1 Announce Type: new 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. Unlike existing LLM-based planners that primarily optimize plan generation, we formulate semantic grounding reliability as a multi-dimensional risk estimation problem. The proposed architecture explicitly models grounding uncertainty through ambiguity, hallucination and semantic-conflict risks before planning occurs, enabling the system to decide whether to execute the instruction, request clarification, or reject it. To evaluate the approach, we introduce TRUST-NAV, a benchmark containing both