Skill Chaining

Skill chaining is a reinforcement learning technique that decomposes complex tasks into simpler sub-tasks (skills), enabling robots and AI agents to solve long-horizon problems by sequentially executing these learned skills. Current research focuses on improving the robustness and efficiency of skill chaining through methods like generative models for planning skill sequences, value-informed state selection for smoother transitions between skills, and adversarial training to handle unforeseen starting states. These advancements are significant for tackling challenging real-world problems, such as surgical robotics and complex manipulation tasks, where traditional reinforcement learning methods struggle due to the vast state and action spaces.