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Events for April 28, 2016
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PhD Defense - Christian Potthast
Thu, Apr 28, 2016 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Defense - Christian Potthast
Thursday, April 28, 2016 @ 10:00 am - 12:00 pm
Computer Science
Title: Information Theoretical Action Selection
Location: RTH 406
Time: 10:00am - 12:00pm , April 28th, 2016
PhD Candidate: Christian Potthast
Committee members:
Prof. Gaurav S. Sukhatme (Chair)
Prof. Stefan Schaal
Prof. Sandeep K. Gupta
Abstract:
For robots to become one day fully autonomous and assist us in our daily life's, they need to be able to self reliantly acquire information about their environment. Challenges arise from limited energy budget to operate the robot, occlusion as well as uncertainty in data captured by noisy sensor. To cope with such challenges, the robot needs to be able to rely on a system that enables him to capture efficiently information and stay well within its constraints. Furthermore, information acquisition should be reactive to sensor measurement, incorporate uncertainty and tradeoff information gain with energy usage.
In my thesis we look at the realization of such systems using well established information theoretical quantities to formulate a framework as general and versatile as possible. Specifically, we look at the task of defining objective functions that enable us to tradeoff information with acquisition cost, enabling the robot to gather as much useful information as possible, but at the same time keep energy consumption to a minimum. We address this challenge for a variety of different tasks.
First, we look at the problem of 3d data acquisition which is of outmost importance to a robotic system since the robot needs to know the environment it is operating in. In this work I propose a framework that enables the robot to quickly acquire information by sequentially choosing next observation positions that maximize information. Next, we look at adaptive action selection in the context of object recognition on robots with limited operating capabilities. I propose an information-theoretic framework that combines and unifies two common techniques: view planning for resolving ambiguities and occlusions and online feature selection for reducing computational costs. Concretely, this framework adaptively chooses two strategies: utilize simple-to-compute features that are the most informative for the recognition task or move to new viewpoints that optimally reduce the expected uncertainties on the identity of the object. Lastly, I present an online trajectory optimization approach that optimizes a trajectory such that object recognition performance is improved. With the idea in mind that the robot needs to make progress towards a goal a cost function is formulated formulated the that allows the robot to improve recognition performance, reduces information acquisition time while simultaneously moving towards the goal point.
Location: Ronald Tutor Hall of Engineering (RTH) - 406
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
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MFD - Chemical Engineering and Materials Science Distinguished Lecture: Kristin Perrson
Thu, Apr 28, 2016 @ 12:45 PM - 02:00 PM
Mork Family Department of Chemical Engineering and Materials Science
Conferences, Lectures, & Seminars
Speaker: Kristin Perrson, Lawrence Berkeley National Laboratory
Talk Title: The Materials Project: Merging Simulations, Supercomputing, and Data Science for Materials Genomics
Series: MFD Distinguished Lecture
Host: Prof. Priya Vashishta
Location: James H. Zumberge Hall Of Science (ZHS) - 159
Audiences: Everyone Is Invited
Contact: Jason Ordonez
