Learning Socially Adaptive Navigation Strategies: Lessons from the SmartWheeler Project
- McGill University
Sept. 26, 2014, 2:30 p.m. - None
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 a smart power
wheelchair developed in our group, called the SmartWheeler.
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.