Abstract: Imitation learning has achieved strong performance in sequential decision-making tasks, but typically requires large numbers of expert demonstrations, has limited generalization capability in unseen scenarios, and is challenging for laypeople without technical backgrounds. This thesis introduces structured policies, a framework that integrates human domain knowledge into imitation learning by using large language models (LLMs) to generate semantically meaningful policy structures from [...]

Abstract: Robot learning approaches such as behavior cloning and reinforcement learning have shown great promise in synthesizing robot skills from human demonstrations in specific environments. However, these approaches often struggle to generalize to unseen real-world settings because they rely on task-specific demonstrations or complex simulators. While foundation models (e.g., LLMs, VLMs) offer rich semantic understanding [...]