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DCL Lecture Series: Angela Schoellig, "Improving the Performance and Autonomy of Mobile Robots by Enabling Them to Learn from Past Experience"

Event Type
Seminar/Symposium
Sponsor
Decision and Control Laboratory, Coordinated Science Laboratory
Location
CSL Auditorium (B02)
Date
Oct 22, 2014   3:00 - 4:00 pm  
Speaker
Prof. Angela Schoellig, University of Toronto
Contact
Heather Glanzer
E-Mail
hglanzer@illinois.edu
Phone
217/333/0433
Views
112
Originating Calendar
CSL Decision and Control Group

Title:  Improving the Performance and Autonomy of Mobile Robots by Enabling Them to Learn from Past Experience

Abstract:  Traditionally, motion planning and control algorithms for robots have been designed based on a priori knowledge about the system and its environment (including models of the robot’s dynamics and maps of the environment). This approach has enabled successful robot operations in predictable environments. However, to achieve reliable robot operations in unknown, changing, and generally uncontrolled environments, we must enable robots to acquire knowledge during operation and adapt their behavior accordingly.

In my talk, I will present learning algorithms that enable robots to improve their performance based on experience – like humans who acquire incredible skills through practice. These learning methods combine ideas from control theory and machine learning: feedback controllers based on a priori model information assure safe operation initially, while based on the data collected during operation the model is refined and the robot’s performance is gradually improved.

I will present experimental results that show successful learning on different robot platforms: (i) a stereo-camera-equipped rover learns to traverse unknown rough terrain and (ii) flying robots learn to race through a slalom course, perform aerobatics, and dance to music.

Biography:Angela Schoellig is an Assistant Professor at the University of Toronto Institute for Aerospace Studies (UTIAS). She conducts research at the interface of robotics, controls and learning. Her goal is to enhance the performance and autonomy of robots by enabling them to learn from past experiments and from each other. She has been developing planning, control and learning algorithms for high-performance robot motions. Experimental validation is an important facet of her work. To this end, she has worked with aerial vehicles for the past six years and, more recently, applied her work to large, outdoor ground vehicles. You can watch her vehicles perform slalom races and flight dances at www.youtube.com/user/angelaschoe.

Angela received her Ph.D. from ETH Zurich (supervised by Prof. Raffaello D’Andrea), and holds both an M.Sc. in Engineering Science and Mechanics from the Georgia Institute of Technology and a Masters degree in Engineering Cybernetics from the University of Stuttgart, Germany. Her Ph.D. was awarded the ETH Medal and the2013 Dimitris N. Chorafas Foundation Award (as one of 35 world-wide). She was selected as the youngest member of the 2014 Science Leadership Program, which promotes outstanding Canadian scientists. In 2013 she was named one of “25 women in robotics you need to know about” by Robohub.org, a leading professional robotics online platform. She was finalist of the 2008 IEEE Fellowship in Robotics and Automation, which supports prospective leaders in this field. Her past research has been published in journals such as Autonomous Robots and the IEEE Robotics & Automation Magazine, and has received coverage worldwide in mainstream TV, print and online media. More information about her research is available at: www.schoellig.name.

link for robots only