RI: Small: Exploiting Global Structure in Robot Decision Problems

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Some decision problems, like loading a dishwasher or assembling furniture, require explicit planning about a sequence of steps, while others, like grasping an object or swerving to avoid a stopped car, can be performed largely through learned reflexes. In robotics, it is still poorly understood why some problems require planning and why others can be solved via reflex. This project explores how techniques from topology, a branch of mathematics, can be applied to study the fundamental nature of robot decision problems. By developing algorithms that apply topology to optimize, analyze, and visualize large datasets of robot motions, the researchers hope to better understand the gray area between planning and learning. Ultimately, this better understanding could help other engineers develop more responsive, capable, and robust robot behaviors. The technical goal of this research is to analyze the mathematical relation connecting robot decision problems to their solutions in order to shed light on fundamental questions surrounding the connection between motion planning and control learning. Breaking from the classical planning paradigm of developing an algorithm that solves individual problem queries, the project studies the global topological characteristics of continuous variations of related problem instances. Specifically, it investigates how certain features of topological complexity relate to the performance of learning and planning algorithms. Based on this understanding, new algorithms, topological metrics, and visualization techniques are developed to help exploit topological structure for faster planning and more accurate learning. The proposed methods are evaluated on benchmark problems in redundant inverse kinematics, legged robots, and agile autonomous vehicles.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.