Joelle Pineau
Learning Socially Adaptive Navigation Strategies: Lessons from the SmartWheeler Project
Associate Professor of Computer Science, McGill University
November 07, 2014
Abstract
A key skill for mobile robots is the ability to navigate efficiently through their environment. In the case of social or assistive robots, this involves navigating through human crowds. Typical performance criteria, such as reaching the goal using the shortest path, are not appropriate in such environments, where it is more important for the robot to move in a socially acceptable manner. In this talk I will describe new methods based on imitation and reinforcement learning which we have developed to allow robots to achieve socially adaptive path planning in human environments.Performance of these methods will be illustrated using experiments with a smart power wheelchair robot called the SmartWheeler.
Speaker Biography
Joelle Pineau is an Associate Professor at the School of Computer Science at McGill University, co-director of the Reasoning and Learning Lab, and member of the Centre for Intelligent Machines (CIM). Before joining McGill in 2004, she received a B.A.Sc. (1998) in Engineering from the University of Waterloo, and an MSc (2001) and PhD (2004) in Robotics from Carnegie Mellon University. Her research focuses on developing new models and algorithms for learning and decision-making in partially observable stochastic domains, and applying these models and algorithms to complex problems in robotics and health-care.
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