LATIDIA · Robótica

VICTR: Aprendizaje contextual basado en valores para modelos de visión, lenguaje y acción.

arXiv: 2611.08698v1Tipo de anuncio: nuevo Resumen: Los recientes avances en los modelos de cimentación de robots han destacado el potencial del aprendizaje en contexto (ICL) para adaptar el comportamiento del robot a partir de demostraciones sin más

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arXiv:2611.08698v1 Announce Type: new Abstract: Recent advances in robot foundation models have highlighted the potential of in-context learning (ICL) for adapting robot behavior from demonstrations without further fine-tuning. Previous work has used retrieval-based post-training to equip pretrained vision-language-action (VLA) models with the capability for ICL by teaching them to condition action predictions on retrieved visually-relevant demonstration chunks. However, the effectiveness of retrieval hinges on selecting context relevant to the current execution stage, which visual similarity alone may not capture. We introduce VICTR, a framework enabling in-context learning in VLAs via progress-informed context retrieval built around an In-Context Value Function Estimator (IC-VFE). IC-VFE conditions on a reference demonstration video, a language