A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements

TL;DR

This paper introduces a flexible, open-source framework for task and motion planning that improves efficiency by combining geometric analysis with existing planners, demonstrated on complex robotic navigation tasks.

Contribution

It presents a novel meta-engine that integrates task and motion planning with topological refinements, enabling efficient problem-solving with off-the-shelf planners.

Findings

Enhanced planning efficiency through geometric pruning.

Successful application to complex navigation scenarios.

Competitive performance with state-of-the-art algorithms.

Abstract

Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem that includes discrete actions executable by low-level continuous motions. This field is gaining increasing interest within the robotics community, as it significantly enhances robot's autonomy in real-world applications. Many solutions and formulations exist, but no clear standard representation has emerged. In this paper, we propose a general and open-source framework for modeling and benchmarking TAMP problems. Moreover, we introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles. This approach enables using any off-the-shelf task planner and motion planner while leveraging a geometric analysis of the motion planner's search space to prune the task planner's exploration, enhancing its efficiency. We also show how to specialize this meta-engine for the case of an incremental SMT-based planner. We demonstrate the effectiveness of our approach across benchmark problems of increasing complexity, where robots must navigate environments with movable obstacles. Finally, we integrate state-of-the-art TAMP algorithms into our framework and compare their performance with our achievements.