Journal
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
Volume 29, Issue 12, Pages 1565-1592Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/0278364910369715
Keywords
Field robotics; mobile robotics; autonomous navigation; learning from demonstration; imitation learning; inverse reinforcement learning
Categories
Funding
- DARPA [MDA972-01-9-0005]
- US Army Research Laboratory [DAAD19-01-2-0012]
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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 complex unstructured terrain, this coupling (in the form of a cost function) has a large impact on robot behavior and performance, necessitating a robust design. This paper explores the application of Learning from Demonstration to this task for the Crusher autonomous navigation platform. Using expert examples of desired 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 and imperfect demonstration 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 effort. Experimental results are presented from autonomous traverses through complex natural environments.
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