Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning

• URL: http://arxiv.org/abs/2202.00907v1
• Date: Wed, 2 Feb 2022 08:11:20 GMT
• Title: Using Deep Learning to Bootstrap Abstractions for Hierarchical Robot Planning
• Authors: Naman Shah, Siddharth Srivastava
• Abstract summary: 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. It uses the learned abstractions in a novel multi-source bi-directional hierarchical robot planning algorithm.
• Score: 27.384742641275228
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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