GCS*: Forward Heuristic Search on Implicit Graphs of Convex Sets
Shao Yuan Chew Chia, Rebecca H. Jiang, Bernhard Paus Graesdal, Leslie, Pack Kaelbling, Russ Tedrake

TL;DR
GCS* is a novel forward heuristic search algorithm designed for large-scale implicit graphs of convex sets, enabling efficient and optimal solutions for complex continuous-discrete planning problems such as motion planning and contact planning.
Contribution
It generalizes A* search to the GCS setting, introducing pruning techniques for cost and reachability, and offers both optimal and suboptimal variants with practical implementations.
Findings
GCS* efficiently solves large-scale motion planning problems.
The method guarantees cost-optimality and completeness in the GCS setting.
It outperforms prior methods on planar pushing tasks with contact mode explosion.
Abstract
We consider large-scale, implicit-search-based solutions to Shortest Path Problems on Graphs of Convex Sets (GCS). We propose GCS*, a forward heuristic search algorithm that generalizes A* search to the GCS setting, where a continuous-valued decision is made at each graph vertex, and constraints across graph edges couple these decisions, influencing costs and feasibility. Such mixed discrete-continuous planning is needed in many domains, including motion planning around obstacles and planning through contact. This setting provides a unique challenge for best-first search algorithms: the cost and feasibility of a path depend on continuous-valued points chosen along the entire path. We show that by pruning paths that are cost-dominated over their entire terminal vertex, GCS* can search efficiently while still guaranteeing cost-optimality and completeness. To find satisficing solutions…
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Taxonomy
TopicsRobotic Path Planning Algorithms · Constraint Satisfaction and Optimization · Vehicle Routing Optimization Methods
