Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning
This addresses the need for efficient and reliable robot planning in new environments without expert intervention, representing a strong specific gain rather than a broad breakthrough.
The paper tackles the problem of automatically learning abstractions for hierarchical robot planning to replace hand-designed ones, showing that their approach reduces planning time by nearly a factor of ten compared to state-of-the-art baselines in unseen test environments.
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. This allows us to compute abstract states and actions for new environments automatically using the critical regions predicted by a deep neural network with an auto-generated robot-specific architecture. We show that the learned abstractions can be used with 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) our learned abstractions provide the information necessary for efficient multi-source hierarchical planning; and that (b) this approach of learning, abstractions, 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.