Applied Imitation Learning for Autonomous Navigation in Complex Natural Terrain

Abstract

Rough terrain autonomous navigation continues to pose a challenge to the robotics community. Robust navigation by a mobile robot depends not only on the individual performance of perception and planning systems, but on how well these systems are coupled. When traversing rough terrain, this coupling (in the form of a cost function) has a large impact on robot performance, necessitating a robust design. This paper explores the application of emph{Imitation Learning} to this task for the Crusher autonomous navigation platform. Using expert examples of proper navigation behavior, mappings from both online and offline perceptual data to planning costs are learned. Challenges in adapting existing techniques to complex online planning systems are addressed, along with additional practical considerations. The benefits to autonomous performance of this approach are examined, as well as the decrease in necessary designer interaction. Experimental results are presented from autonomous traverses through complex natural terrains.