Robot learning, a research area at the intersection of machine learning and robotics, is driven by the goal of building robotic systems that ca learn from experience to perform complex tasks. Research in this area is motivated by challenges that arise from acting in stochastic unstructured environments, while receiving noisy and high-dimensional
input data such as images. Machine learning provides powerful tools for solving problems in robotics that, due to their complexity, cannot be addressed solely by engineering. For efficiency and due to the high cost of collecting data in robotics, most practical methods are model-based. In other terms, the robot explicitly learns geometric and mechanical models that can help identify objects and predict how they would move. In this talk, I will present my ongoing work in the field of robot learning, which is centered around three main axes: (1) learning physics models of objects, (2) learning visual and geometric models of objects, and (3), learning to manipulate objects from human demonstrations.
Abdeslam Boularias received the engineering degree in computer science from the École Nationale Supérieure d’Informatique (ESI) in Algeria in 2004, the Master’s degree in computer science from University of Paris-Sud in France in 2005, and the Ph.D. degree from Laval University in Canada in 2010. From August 2010 to April 2013, he was a Research Scientist with the Empirical Inference Department at the Max Planck Institute for Intelligent Systems in Germany. From May 2013 to July 2015, he was a Postdoctoral Fellow and a Project Scientist at Carnegie Mellon University. He is an assistant professor in the department of computer science at Rutgers University since September 2015. His main research interests include planning under uncertainty, reinforcement learning, and robotics.