LATIDIA · Robótica

Aprendizaje seguro para tareas de robots ricos en contactos: una encuesta desde métodos clásicos basados en el aprendizaje hasta modelos de cimientos seguros

arXiv:2512.11908v3 Tipo de anuncio: reemplazar Resumen: Las tareas ricas en contactos plantean desafíos significativos para los sistemas robóticos debido a la incertidumbre inherente, la dinámica compleja y el alto riesgo de daños durante la interacción.

La noticia

arXiv:2512.11908v3 Announce Type: replace Abstract: Contact-rich tasks pose significant challenges for robotic systems due to inherent uncertainty, complex dynamics, and the high risk of damage during interaction. Recent advances in learning-based control have shown great potential in enabling robots to acquire and generalize complex manipulation skills in such environments, but ensuring safety, both during exploration and execution, remains a critical bottleneck for reliable real-world deployment. This survey provides a comprehensive overview of safe learning-based methods for robot contact-rich tasks. We categorize existing approaches into two main domains: safe exploration and safe execution. We review key techniques, including constrained reinforcement learning, risk-sensitive optimization, uncertainty-aware modeling, control barrier functions, and model predictive