LATIDIA · Robótica

Planificación de tareas de disparo cero incorporada a través de la integración LLM-VLM consciente del hardware para la manipulación robótica con conexión a tierra

arXiv: 2610.14106v1Tipo de anuncio: nuevo Resumen: Los modelos de lenguaje grandes pueden traducir instrucciones en lenguaje natural en planes de manipulación plausibles, pero la ejecución robótica requiere decisiones basadas físicamente que respeten

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arXiv:2610.14106v1 Announce Type: new Abstract: Large language models can translate natural-language instructions into plausible manipulation plans, but robotic execution requires physically grounded decisions that respect robot embodiment, workspace geometry, reachability, collision constraints, and grasp feasibility. This paper presents a hardware-aware large language model-vision-language model (LLM-VLM) manipulation framework for a simulated Franka Research 3 robot in NVIDIA Isaac Sim. The framework separates symbolic reasoning from metric execution: an API-hosted LLM planner receives a structured scene summary and generates only symbolic task information, including the object to manipulate, target relation, approach preference, and objects to avoid. A local Qwen3-VL 8B VLM verifier supports semantic confirmation of ambiguous objects and open-container surrogates. Both