Priberam Machine Learning Lunch Seminar: "Decision-theoretic Planning under Uncertainty for Active Cooperative Perception"
Date: Tuesday, June 8th, 2010
Speaker: Matthijs Spaan (ISR)
Venue: IST Alameda, Sala PA2 (Edifício de Pós-Graduação)
Lunch will be provided
As robots leave research labs to operate more often in human-inhabited, larger environments, cooperation between sensor networks and mobile robots becomes crucial. For example, in urban scenarios, employing mobile robots is a need to augment the limited sensor coverage and improve detection and tracking accuracy. The fusion of sensory information between fixed surveillance cameras and each robot, with the goal of maximizing the amount and quality of perceptual information available to the system can be called cooperative perception. A promising decision-theoretic planning framework for active cooperative perception is that of Partially Observable Markov Decision Processes (POMDPs). The suitability of POMDPs for the previously depicted scenario arises from their ability to inherently trade off task completion, which could be react to a potential event that has been detected, and information gathering in a efficient way, that is decide to send a robot to improve situational awareness. In this talk we will discuss how planning under uncertainty can be applied to active cooperative perception problems.
Matthijs Spaan received a M.Sc. (2002) in AI and a Ph.D. (2006) in CS from the Universiteit van Amsterdam, The Netherlands. Currently he is a research scientist at the Institute for Systems and Robotics, Instituto Superior Técnico, Lisbon, Portugal, and he is the principal investigator of a national project on "Decentralized Planning Under Uncertainty for Cooperative Systems". His thesis was on "Approximate planning under uncertainty in partially observable environments" and was selected as a runner-up for EURON's 7th Georges Giralt PhD Award. His scientific interests are in planning under uncertainty, sequential decision making, autonomous robots, cooperative multiagent/multi-robot systems, (decentralized) partially observable Markov decision processes (POMDPs/Dec-POMDPs), reinforcement learning, machine learning and AI in general.