Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning

This paper addresses the problem of learning abstractions that boost robot planning performance while providing strong guarantees of reliability. Although state-of-the-art hierarchical robot planning algorithms allow robots to efficiently compute long-horizon motion plans for achieving user desired tasks, these methods typically rely upon environment-dependent state and action abstractions that need to be hand-designed by experts. We present a new approach for bootstrapping the entire hierarchical planning process. It shows how abstract states and actions for new environments can be computed automatically using the critical regions predicted by a deep neural-network with an auto-generated robot specific architecture. It uses the learned abstractions in a novel multi-source bi-directional hierarchical robot planning algorithm that is sound and probabilistically complete. An extensive empirical evaluation on twenty different settings using holonomic and non-holonomic robots shows that (a) the learned abstractions provide the information necessary for efficient multi-source hierarchical planning; and that (b) this approach of learning abstraction and planning outperforms state-of-the-art baselines by nearly a factor of ten in terms of planning time on test environments not seen during training.