PhD Thesis Defense: David Silver
Learning Preference Models for Autonomous Mobile Robots in Complex Domains
Carnegie Mellon University
September 13, 2010, 12:30 p.m., NSH 1507
Abstract
Achieving robust and reliable autonomous operation even in complex unstructured environments is a central goal of field robotics. ...
This thesis presents the development and application of machine learning techniques that automate the construction and tuning of preference models within complex mobile robotic systems. Utilizing the framework of inverse optimal control, expert examples of robot behavior can be used to construct models that generalize demonstrated preferences and reproduce similar behavior. Novel learning from demonstration approaches are developed that offer the possibility of significantly reducing the amount of human interaction necessary to tune a system, while also improving its final performance. Techniques to account for the inevitability of noisy and imperfect demonstration are presented, along with additional methods for improving the efficiency of expert demonstration and feedback.
The effectiveness of these approaches is confirmed through application to several real world domains, such as the interpretation of static and dynamic perceptual data in unstructured environments and the learning of human driving styles and maneuver preferences. ... These experiments validate the potential applicability of the developed algorithms to a large variety of future mobile robotic systems.
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