Autonomous Robot Skill Acquisition

Among the most impressive of aspects of human intelligence is skill acquisition--the ability to identify important behavioral components, retain them as skills, refine them through practice, and apply them in new task contexts. Skill acquisition underlies both our ability to choose to spend time and effort to specialize at particular tasks, and our ability to collect and exploit previous experience to become able to solve harder and harder problems over time with less and less cognitive effort. Hierarchical reinforcement learning provides a theoretical basis for skill acquisition, including principled methods for learning new skills and deploying them during problem solving. However, existing work focuses largely on small, discrete problems. This dissertation addresses the question of how we scale such methods up to high-dimensional continuous domains, in order to design robots that are able to acquire skills autonomously. This presents three major challenges we introduce novel methods addressing each of these challenges. First, how does an agent operating in a continuous environment discover skills Although the literature contains several methods for skill discovery in discrete environments, it offers none for the general continuous case. We introduce skill chaining, a general skill discovery method for continuous domains. Skill chaining incrementally builds a skill tree that allows an agent to reach a solution state from any of its start states by executing a sequence or chain of acquired skills. We empirically demonstrate that skill chaining can improve performance over monolithic policy learning in the Pinball domain, a challenging dynamic and continuous reinforcement learning problem. Second, how do we scale up to high-dimensional state spaces While learning in relatively small domains is generally feasible, it becomes exponentially harder as the number of state variables grows.