Lazy Evaluation and Subsumption Caching for Search-Based Integrated Task and Motion Planning

State of the art classical planning systems can efficiently solve large symbolic problem instances. Applying classical planning techniques to robotics is possible by integrating geometric reasoning in the planning process. The problems that are solvable in this way are significantly smaller than purely logical formulations as many costly geometric calculations are requested by a planner. Therefore we aim to avoid those calculations while preserving correctness. We address this problem with efficient caching techniques. Subsumption caching avoids costly computations by caching geometric queries and beyond answering the same queries also considers less or more constrained ones. Additionally, we describe a lazy evaluation technique that pushes applicability checks for successor states performing geometric queries to a later point. As we are interested in the performance of our planner not as a standalone component, but as part of an intelligent robotic system, we evaluate those techniques embedded in an integrated system during real-world mobile manipulation experiments.