[Lisa_seminaires] Fwd: Rappel: jeudi 19 janvier, Lantao Liu (USC)

Yoshua Bengio yoshua.umontreal at gmail.com
Mer 18 Jan 22:16:34 EST 2017


Interesting seminar tomorrow by a potential new faculty hire in the area of
robotics.

---------- Forwarded message ----------
From: Pierre McKenzie <mckenzie at iro.umontreal.ca>
Date: 2017-01-18 21:51 GMT-05:00
Subject: Rappel: jeudi 19 janvier, Lantao Liu (USC)
To: seminaires at iro.umontreal.ca


Bienvenue à tous!

-------- Forwarded Message --------
Subject: colloque DIRO, jeudi 19 janvier, Lantao Liu (USC)
Date: Mon, 16 Jan 2017 14:20:32 -0500
From: Pierre McKenzie <mckenzie at iro.umontreal.ca>
<mckenzie at iro.umontreal.ca>
To: seminaires at iro.umontreal.ca

* From Single-Robot Planning to Multi-Robot Collective Decision-Making*

par


*Lantao Liu *

 University of Southern California

*Jeudi 19 janvier, 15:30-16:30*, *Salle 3195*, Pavillon André-Aidenstadt

    Université de Montréal, 2920 Chemin de la Tour

Café avant 15:00-15:30

*Cette présentation sera donnée en anglais.*



*Résumé:*

Effectively understanding the surrounding environment and efficiently
interacting with it are important functions for autonomous systems, where
in a broad sense the environment consists of not only static objects but
also dynamic things such as wind, fluid, and other robot teammates. The
non-static environment also indicates that the underlying environmental
model is time-variant, with possibly high uncertainty in both space and
time 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., fluid flows, air turbulences). 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 information-rich data. 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 re-interprets 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.

*Biographie:*

Dr. Lantao Liu is a Postdoctoral Scholar - Research Associate in the
Department of Computer Science at the University of Southern California.
Before his research experience at USC, Dr. Liu worked as a Postdoctoral
Fellow in the Robotics Institute at Carnegie Mellon University. He
received a 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.
-------------- section suivante --------------
Une pièce jointe HTML a été nettoyée...
URL: http://webmail.iro.umontreal.ca/pipermail/lisa_seminaires/attachments/20170118/5c94d874/attachment.html 


Plus d'informations sur la liste de diffusion Lisa_seminaires