Modular Skill

Modular skill research focuses on decomposing complex tasks into smaller, reusable skill units for improved robot learning and control. Current efforts concentrate on developing methods for efficiently sequencing and blending these skills, often employing optimization-based approaches, transformer architectures, or multi-adapter models to manage and combine them. This modular approach enhances generalization, sample efficiency, and interpretability in both robotic manipulation and natural language processing tasks, leading to more robust and adaptable intelligent systems.