Learning to guide task and motion planning using score-space representation
This addresses efficiency challenges in task and motion planning, which is incremental as it builds on existing planning methods.
The paper tackles the problem of speeding up search in task and motion planning by proposing a learning algorithm that predicts constraints based on a score-space representation, resulting in performance orders of magnitude faster than an unguided planner.
In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems. Our algorithm proposes solutions to three different challenges that arise in learning to improve planning efficiency: what to predict, how to represent a planning problem instance, and how to transfer knowledge from one problem instance to another. We propose a method that predicts constraints on the search space based on a generic representation of a planning problem instance, called score-space, where we represent a problem instance in terms of the performance of a set of solutions attempted so far. Using this representation, we transfer knowledge, in the form of constraints, from previous problems based on the similarity in score space. We design a sequential algorithm that efficiently predicts these constraints, and evaluate it in three different challenging task and motion planning problems. Results indicate that our approach performs orders of magnitudes faster than an unguided planner