LATIDIA · Robótica

Modelos adaptativos de campo continuos (CFAM) para la IA física posterior al despliegue

arXiv:2609.04552v2 Tipo de Anuncio: reemplazar Resumen: Autonomía interactiva desatendida - máquinas que entran en peligro en lugar de humanos y completan tareas con herramientas humanas - sigue siendo una capacidad faltante en la misión crítica

La noticia

arXiv:2609.04552v2 Announce Type: replace Abstract: Unattended interactive autonomy - machines that step into danger in place of humans and complete tasks with human tools - remains a missing capability in mission-critical operations. These domains offer scarce training data and only onboard compute, yet deployed systems must face novelty without erasing prior competence. We introduce Continual Field-Adaptive Models (CFAMs), which learn efficiently in the lab and continue learning after deployment through autonomous, gradient-free, on-device updates. CFAM uses a complementary learning architecture with a frozen slow-learning component and a fast-learning Capsule Field. The slow component contains three cortices: Sensor, which maps multimodal input into 3D-grounded geometry; Reasoning, which decomposes tasks into skills