LATIDIA · Robótica

Aprendiendo de la Experiencia Física:Adaptación Contexto-Espacio para la Manipulación Robótica

arXiv:2610.41320v1 Anuncio Tipo: nuevo Resumen: Los modelos fundacionales multimodales recientes exhiben fuertes capacidades de percepción, razonamiento y aprendizaje en contexto, pero la traducción de estas capacidades a acciones físicas sigue siendo un desafío.

La noticia

arXiv:2610.41320v1 Announce Type: new Abstract: Recent multimodal foundation models exhibit strong perception, reasoning, and in-context learning capabilities, but translating these capabilities into physical action remains challenging. Through real-world experiments across diverse manipulation tasks, we find that ChatGPT-6-Astra, a state-of-the-art general-purpose reasoner, can serve as a physically actionable, context-responsive manipulation policy, demonstrating effective fine manipulation and spatial reasoning. Building on these findings, we introduce LEXP, a harness for learning from physical experience through context-space adaptation. LEXP combines task-level planning with an experience module that interprets execution evidence, extracts and retains lessons with applicability conditions, and reuses relevant lessons to generate contextual feedback for subsequent actions. Model weights and the action interface