Speaker: Liam Paull, MIT
Title: Robots in the Real World: Accounting for Resource Constraints To Achieve Tasks
Robots that operate in the physical world have finite resources and hard realtime constraints. For autonomous systems to be able to perform useful tasks, their algorithms must scale well with time, space, and complexity. In this talk I will present a framework for resource-constrained graphical inference. The approach optimally utilizes available computation, memory, and bandwidth to provide the best possible estimates of the state of the system given the limited resources. The flexibility of the framework is demonstrated in two diverse application domains: underwater cooperative localization and mapping with communication constraints, and collision-free navigation with computation and memory constraints. In both cases, we exploit task-specific structure to optimize task performance without exceeding the finite available resources.
Liam Paull received the B.Sc. degree in computer engineering from McGill University, Montreal, QC, Canada, in 2004 and the Ph.D. degree in elec- trical and computer engineering from University of New Brunswick, Fredericton, NB, Canada, in 2013. He is a Research Scientist in the Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. His research interests include robust inference and control for resource-constrained and safety-critical robotic systems including both marine and autonomous vehicle applications.
Talk sponsored by: Mathematical and Computational Sciences, UTM
For Additional Information contact Steve Easterbrook