Speaker: Lantao Liu Indiana University - Bloomington
Time: 2018-05-11 13:30-2018-05-11 14:30
Venue: MMW S-327

Abstract:

Effectively understanding the surrounding environment and efficiently interacting with it are important functions for autonomous robots, where in a broad sense the unstructured environment consists of not only static objects but also dynamic things such as winds, water flows, even the robot teammates. The non-static environment also indicates that the underlying environmental model can be time-variant, with possibly high uncertainty in both spatial and temporal dimensions. In this talk, I will first introduce a decision-making framework called time-varying Markov Decision Process, which can be used to cope with robot's time-varying action uncertainty caused by dynamic disturbances (e.g., turbulences in water, air). Then I am going to describe a data driven planning and learning method for long-term environment sensing and monitoring, where I use techniques of Gaussian Processes, information theory and Bayesian inference to navigate the robot to explore unknown habitats and collect data with maximal information. After that, I will discuss a cooperative stochastic planning framework for multi-robot systems. This coordination method takes advantage of a classic optimal assignment problem, and reinterprets the underlying matching mechanism so that it is used to decouple and approximate the original computational prohibitive stochastic problem. Experimental and simulation results on both underwater and aerial vehicles will be included in this talk.

Short Bio:

Lantao Liu is an Assistant Professor in the Department of Intelligent Systems Engineering at Indiana University - Bloomington. He was a Research Associate in the Department of Computer Science at University of Southern California during 2015-2017. Prior to that, he worked as a Postdoctoral Fellow in the Robotics Institute at Carnegie Mellon University during 2013-2015. Dr. Liu received his Ph.D. from the Department of Computer Science and Engineering at Texas A&M University in 2013, and a Bachelor degree from the Department of Automatic Control at Beijing Institute of Technology in 2007. Dr. Liu's research interests include planning, decision-making, and applied machine learning methods for autonomous robotic systems, as well as distributed or decentralized coordination approaches for multi-robot or swarm systems.