Experience-Based Planning with Sparse Roadmap Spanners

TL;DR

Thunder is an experience-based planning framework that efficiently reduces computation time for high-dimensional, constraint-rich planning problems by leveraging a sparse, graph-based experience storage method, outperforming previous path-based approaches.

Contribution

The paper introduces Thunder, a novel experience-based planning method using a sparse roadmap spanner to improve efficiency and scalability over prior path-based experience storage methods.

Findings

Thunder is on average ten times faster than Lightning.

Thunder uses 98.8% less memory after 10,000 trials.

Demonstrated on a 30-DOF humanoid robot in complex environments.

Abstract

We present an experienced-based planning framework called Thunder that learns to reduce computation time required to solve high-dimensional planning problems in varying environments. The approach is especially suited for large configuration spaces that include many invariant constraints, such as those found with whole body humanoid motion planning. Experiences are generated using probabilistic sampling and stored in a sparse roadmap spanner (SPARS), which provides asymptotically near-optimal coverage of the configuration space, making storing, retrieving, and repairing past experiences very efficient with respect to memory and time. The Thunder framework improves upon past experience-based planners by storing experiences in a graph rather than in individual paths, eliminating redundant information, providing more opportunities for path reuse, and providing a theoretical limit to the size of the experience graph. These properties also lead to improved handling of dynamically changing environments, reasoning about optimal paths, and reducing query resolution time. The approach is demonstrated on a 30 degrees of freedom humanoid robot and compared with the Lightning framework, an experience-based planner that uses individual paths to store past experiences. In environments with variable obstacles and stability constraints, experiments show that Thunder is on average an order of magnitude faster than Lightning and planning from scratch. Thunder also uses 98.8% less memory to store its experiences after 10,000 trials when compared to Lightning. Our framework is implemented and freely available in the Open Motion Planning Library.