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                                                                                          CSE Seminars (2017 Spring) 포스터보기

2017.05.24(Wed)
"An Exploratory Study of Marking Menu Selection by Visually Impaired Participants "
김기범 교수(계명대학교)
2017.05.31(Wed)
"Balance, Continity, Detail"
송희경 의원(자유한국당)
2017.06.07(Wed)
"Attention, Context and Knowledge in Recurrent Neural Networks"
최희열 교수(한동대학교)
2017.05.24(Wed)
"Interactions on Various Display Types"
김기범 교수(계명대학교)

글번호
5693351
일 자
16.08.22
조회수
351
글쓴이
학과사무실
제목 : 2016.12.20(Tue) 'Leveraged Gaussian Process Regression:  Learning from Do's and Don'ts' - Songhwai Oh 오성회교수 (Seoul National University 서울대학교)
-Titile:Leveraged Gaussian Process Regression:  Learning from Do's and Don'ts

-Biograpy 

Songhwai Oh received the B.S. (with highest honors), M.S., and Ph.D. degrees in electrical engineering and computer sciences from the University of California, Berkeley, in 1995, 2003, and 2006, respectively. He is currently an Associate Professor in the Department of Electrical and Computer Engineering, Seoul National University, Seoul, Korea. Before his Ph.D. studies, he was a Senior Software Engineer at Synopsys, Inc. and a Microprocessor Design Engineer at Intel Corporation. In 2007, he was a Postdoctoral Researcher in the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley. From 2007 to 2009, he was an Assistant Professor of electrical engineering and computer science in the School of Engineering, University of California, Merced. His research interests include robotics, computer vision, cyber-physical systems, and machine learning.


-Abstract

With recent advances in hardware, sensing, and algorithms, we are  witnessing the emergence of a new robotics industry. I will present a  few examples of new services provided by upcoming service robots. With the introduction of new service robots in diverse domains, we can expect
that more service robots will be assisting us in the near future in places, such as offices, malls, and homes. But, for a robot to coexist with humans and operate successfully in crowded and dynamic
environments, a robot must be able to learn from experiences to act safely and harmoniously with human participants in the environment. I will discuss research challenges for service robots and our attempts to address those challenges.
In particular, I will present our recent work on learning with counterexamples to enhance safety and social acceptability of service robots. While existing learning from demonstration (LfD) algorithms
assume that demonstrations are given from skillful experts, the proposed method alleviates such assumption by allowing demonstrations from casual or novice users. To learn from demonstrations of mixed quality, we present a sparse-constrained leveraged optimization algorithm using
proximal linearized minimization. I will also present how the same concept can be applied to inverse reinforcement learning to improve performance.


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