Optimizing the Design of Robot Environments via Interleaved Optimization and White-Box Motion-Planning

Abstract

Motion planning algorithms lie at the heart of all robotic systems. While each planner optimizes a different utility function, the robot’s environment is consistently the key factor affecting said utilities. Furthermore, in many scenarios (such as an assembly line) this environment is under human control. Enter our first key insight: rather than treating a motion planner simply as a tool to be applied in a challenging environment, we can use the utilities returned by the planner to optimize the design of the environment itself. To do so, we propose integrating these motion planning algorithms into gradient-free optimization loops that operate over the design space of the robot’s environment. These algorithms are, unfortunately, compute-hungry and thus will dominate the runtime of the optimization process. This motivates our second key insight: rather than treating the motion planner as a black box, we can instead use the operation of an outer optimizer to restrict the work done by an inner graph-based planner. We dub this tight integration between the optimizer and motion planner a “white box” optimization approach, and propose two methods of actualizing it. We note that each of these methods is adapted to the specific gradient-free optimizer being used, underscoring the need for tight integration between it and the motion planner.