Skill Based

Skill-based approaches are transforming robotics and AI by decomposing complex tasks into smaller, reusable skills, enabling more flexible and adaptable systems. Current research focuses on efficient skill integration methods, including reinforcement learning algorithms and neuroevolutionary techniques, often incorporating planning and knowledge representation to improve data efficiency and robustness. This work is significant for advancing autonomous systems, particularly in industrial robotics and language model training, by facilitating faster task adaptation, improved data utilization, and more reliable system verification.