Robot Motion Planning: Challenges and Opportunities for Increasing Robot Autonomy

Abstract

Robot manipulators are increasingly deployed outside of carefully controlled factory settings. Advances in robot motion planning have made it possible to compute feasible motions for more complex systems. My work is focused on enabling planning over varying time horizons subject to complex soft and hard constraints. The goal is to reduce the amount of user input required to command a robot and enable ever greater levels of autonomy. In this presentation I will first give a brief overview of sampling-based motion planning, a class of methods that has been successfully applied to a broad range of complex systems. I will present recent results that show that satisfying hard constraints can be decoupled from the particular planning strategy, which can lead to surprising performance improvements. Next, I will present some results on using hyperparameter optimization to select and tune motion planning algorithms for a given robot. Finally, will present some initial results on supervised autonomy that combines motion planning with compliant control, perception, and human input.