Learned Critical Probabilistic Roadmaps for Robotic Motion Planning
This work addresses the problem of slow motion planning for robots in complex environments, offering a significant speedup that is incremental but highly impactful for practical applications.
The paper tackles the inefficiency of uniform sampling in robotic motion planning by identifying critical states using graph-theoretic techniques and learning to predict them from local features, achieving up to three orders of magnitude improvement in performance while maintaining theoretical guarantees.
Sampling-based motion planning techniques have emerged as an efficient algorithmic paradigm for solving complex motion planning problems. These approaches use a set of probing samples to construct an implicit graph representation of the robot's state space, allowing arbitrarily accurate representations as the number of samples increases to infinity. In practice, however, solution trajectories only rely on a few critical states, often defined by structure in the state space (e.g., doorways). In this work we propose a general method to identify these critical states via graph-theoretic techniques (betweenness centrality) and learn to predict criticality from only local environment features. These states are then leveraged more heavily via global connections within a hierarchical graph, termed Critical Probabilistic Roadmaps. Critical PRMs are demonstrated to achieve up to three orders of magnitude improvement over uniform sampling, while preserving the guarantees and complexity of sampling-based motion planning. A video is available at https://youtu.be/AYoD-pGd9ms.