CMU talk: Imitation Learning for Autonomous Navigation in Complex Natural Terrain

Imitation Learning for Autonomous Navigation in Complex Natural Terrain

David Silver, PhD Candidate
Robotics Institute
Carnegie Mellon University

Thursday, March 19th, 2009


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 talk presents the application of imitation learning to this task. Using expert examples of proper navigation behavior, mappings from both online and offline perceptual data to planning costs are learned. Experimental results are presented from the Crusher autonomous navigation platform, demonstrating a benefit to autonomous performance as well as a decrease in programmer interaction.