Hi all,
I will be taking over the role of arranging tea talks after Razvan
graduates and leaves the lab (Congrats, Razvan!) I have a few announcements
to make here.
(1) If you want to give a teatalk:
Please, feel free to contact me about it. As far as I understand, a topic
for a teatalk may include;
(a) your own work
(b) others' work that may interest the members of the lab
(c) some new ideas that others may have interest in
I'd especially like to emphasize (b). Often, it's easier and faster to
understand the concept of a paper, if someone explains it in 'spoken'
words, rather than 'written' ones. This will help all the members keep up
with the latest and important topics in the field.
Also, if you're hosting a visitor at the lab and she/he's willing to give a
talk, do let me so that I can book a time and place in advance.
(2) Prof. Alain Tapp: Wednesday 21 June @13.00 AA3195
The title and abstract are not available yet, but I will send an email once
more when I have them.
(3) Dustin Webb: Friday 23 June @13.00 AA3195
Title: Online Parameter Estimation via Real-Time Replanning of Continuous
Gaussian POMDPs
Abstract: An accurate dynamics model of a robot is an important ingredient
of many algorithms used to solve robotics problems, including motion
planning, control, localization, and mapping. Models derived from first
principles often contain parameters (e.g. mass, moment of inertia, arm
lengths, etc.) for which values are unknown. Those which cannot be easily
measured must be estimated from the observed behavior of the robot. A good
approach to address this problem is to plan control policies for the robot
that elicit maximal amounts of information about the parameters of the
system, while still achieving other objectives specified for the robot. In
case of parameters subject to drift, this must be done continuously over
the lifetime of the robot if costly re-calibrations are to be avoided. In
this paper, we introduce a new method that formulates the parameter
estimation problem as a continuous partially-observable Markov decision
process (POMDP), which plans control policies that optimally trade-off the
effort spent on learning parameters and effort spent on achieving regular
robot objectives (exploration vs. exploitation), and allow for online,
continual parameter estimation. While POMDPs have, until recently, been
mostly of theoretical interest due to their inherent complexity, we build
on recent advances that allow continuous, Gaussian POMDPs to be
approximately-optimally solved in near-real-time rates. We show that the
computed control policies lead to improved convergence of the belief of the
parameters compared to system identification approaches based on applying
random controls.
Dustin J. Webb, Kyle L. Crandall and Jur van den Berg. Online Parameter
Estimation via Real-Time Replanning of Continuous Gaussian POMDPs. ICRA
2014. http://arl.cs.utah.edu/pubs/ICRA2014-ope.pdf
