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