Information-Theoretic Approaches to Active Sensing: Theory and Practice
Date:
[Link to Slides]
[Link to Event]
Many mobile robots today are capable of collecting information in the form of sensor readings. However, what sensor readings lead to “desirable” information and how to collect them are both ambiguous and challenging questions; such information could be on parameters of an unknown vector field, or hidden intent of traffic participants on a public road. Information-theoretic active sensing, in which a robot closes the loop from perception to action in search of optimal control under an information-theoretic cost, sheds light on those questions from an optimization perspective. The key components in the information-theoretic active sensing is the online inference of partially observable stochastic processes and the effective planning to reduce their uncertainty. In this talk, I will summarize recent and ongoing projects on this topic within the Multi-Robot Systems Lab at Stanford University. The algorithmic strategies range from RRT path planning to Belief MDP, and the applications include environmental monitoring, target tracking, and intent inference.