Back to the Future of Cognition and Control in Robotics

Abstract: This talk will describe a vision and philosophy for research in robot systems. I will do so by drawing on my prior expertise in designing integrated architectures for knowledge representation, reasoning, control, and learning in robotics. These architectures computationally encode key principles such as step-wise iterative refinement and ecological rationality, and leverage the complementary strengths of knowledge-based and data-driven reasoning and learning methods. I will illustrate the capabilities of these architectures in the context of three fundamental problems: vision-based planning and scene understanding, changing-contact robot manipulation, and ad hoc teamwork. In particular, I will emphasize the benefits of exploiting the interplay between reasoning, learning, and control. I will end with a brief discussion of some future research directions.

 

Bio: Prof. Mohan Sridharan is a Chair in Robot Systems in the School of Informatics at the University of Edinburgh (UK). Prior to his current appointment, he held academic positions at the University of Birmingham (UK), The University of Auckland (NZ) and at Texas Tech University (USA). Prof. Sridharan received his Ph.D. from The University of Texas at Austin (USA). His research interests include knowledge representation, cognitive systems, control systems, and interactive learning, as applied to robots and agents collaborating with humans. He is also interested in developing algorithms for automation in non-robotics domains such as climate informatics, agriculture, and transportation. 

Web page: https://homepages.inf.ed.ac.uk/msridhar/