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Presenter: Changhyun Choi (Homepage)
Friday July 18, 2014 from 1:00pm to 2:00pm
* Alternate Location: Levine 307*
As robotic systems move from
well-controlled settings to increasingly unstructured
environments, they are required to operate in highly
dynamic and cluttered scenarios. Finding an object,
estimating its pose, and tracking its pose over time
within such scenarios are challenging problems. Although
various approaches have been developed to tackle these
problems, the scope of objects addressed and the
robustness of solutions remain limited.
Changhyun Choi is a Ph.D. candidate in the School of Interactive Computing, College of Computing at the Georgia Institute of Technology (Georgia Tech) in Atlanta, Georgia, USA. He is also affiliated with the Institute for Robotics and Intelligent Machines (IRIM) at Georgia Tech. He was a research intern in the Imaging Group of Mitsubishi Electric Research Labs (MERL) in Cambridge, Massachusetts, USA, an intern researcher at the Imaging Media Research Center (IMRC) at Korea Institute of Science and Technology (KIST), and an undergraduate researcher at the Intelligent Systems Research Center at Sungkyunkwan University in Korea. He holds a B.S. in Information and Communication Engineering from Sungkyunkwan University. His broad research interests are in visual perception for robotics, with a focus on object recognition and pose estimation, visual tracking, and 3D registration.
Presenter: Sertac Karaman (Homepage)
Friday December 5, 2014 from 11:00am to 12:00pm
Consider a robotic vehicle that is traveling in a cluttered environment or attempting to fulfill tasks that require visiting several locations. What is the maximum speed that this vehicle can achieve and maintain for a long time? How does this speed depend on the agility, perception, actuation, or computation capabilities of the vehicle?
Sertac Karaman is the Charles Stark Draper Assistant Professor of Aeronautics and Astronautics at the Massachusetts Institute of Technology (since Fall 2012). He has obtained B.S. degrees in mechanical engineering and and in computer engineering from the Istanbul Technical University, Turkey, in 2007, an S.M. degree in mechanical engineering from MIT in 2009, and a Ph.D. degree in electrical engineering and computer science also from MIT in 2012. His research interests lie in the broad areas of robotics and control theory. In particular, he studies the applications of probability theory, stochastic processes, stochastic geometry, formal methods, and optimization for the design and analysis of high-performance cyber-physical systems. He is the recipient of an AIAA Graduate Award (2011), an NVIDIA fellowship (2011), as well as an NSF CAREER award (2014).
Presenter: Hong Zhang (Homepage)
Friday November 21, 2014 from 11:00am to 12:00pm
One area of significant progress in robotics research in recent years has been the use of visual sensing by mobile robots to map an environment and negotiate routes within the environment. This growth is mostly attributed to the rich textural information in visual sensory data compared with the traditional range data, as well as to the great strides made in the development of efficient and robust computer vision and image processing algorithms.
Dr. Hong Zhang received his Ph.D. from Purdue University in 1986 in Electrical Engineering. Subsequently he spent 18 months with the GRASP Lab at the University of Pennsylvania as post-doctoral fellow before joining the University of Alberta, Canada where he is currently a Professor in the Department of Computing Science and the Director of the Centre for Intelligent Mining Systems.
Dr. Zhang’s research interest spans robotics, computer vision, and image processing, and he has published close to 200 papers in top international journals and conferences in these areas. He is mostly known for his research in collective robotics - introducing biologically inspired solutions to decentralized control of a multirobot system - and in applied image processing for Alberta’s oil sands industry to measure key performance indicators, creating a unique expertise that never existed before. His current research focuses on visual robot navigation and its application to environment monitoring in Canadian outdoors.
Dr. Zhang has been active in professional activities. His journal editorial responsibilities include IEEE Transaction on Cybernetics, the journal of Robotics, and International Journal of Robotics and Biomimetics. Dr. Zhang chairs the technical committee on Robotics and Intelligent Sensing of the IEEE SMC Society, and is a member of the AdCom of the IEEE Robotics and Automation Society (2014-2016). Among the many conference organization involvements, he serves as the General Chair of 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), to be held in Vancouver, Canada. Dr. Zhang's achievements have been recognized through many awards including IEEE Millennium Medal in 2000, the 2006 Canadian Image Processing and Pattern Recognition Society (CIPPRS) Award for Research Excellence, and a 2008 ASTech Award in recognition of his contributions
to industrial research in Alberta. He is the holder of an NSERC Industrial Research Chair and a Fellow of IEEE.
Presenter: Kris Hauser (Homepage)
Friday November 14, 2014 from 11:00am to 12:00pm
planning -- the problem of computing physical actions to complete a
specified task -- has inspired some of the most theoretically rigorous
and beautiful results in robotics research. But as robots proliferate
in real-world applications like household service, driverless cars,
warehouse automation, minimally-invasive surgery, search-and-rescue, and
unmanned aerial vehicles, we are beginning to
see the classical theory fall behind. The clean assumptions of theory
are at odds with the dirty reality: robots must handle large amounts of
noisy sensor data, unc
Kris Hauser is an Associate Professor at the Pratt School of Engineering at Duke University with a joint appointment in the Electrical and Computer Engineering Department and the Mechanical Engineering and Materials Science Department. He received his PhD in Computer Science from Stanford University in 2008, bachelor's degrees in Computer Science and Mathematics from UC Berkeley in 2003, and worked as a postdoctoral fellow at UC Berkeley. He then joined the faculty at Indiana University from 2009-2014, where he started the Intelligent Motion Lab. He is a recipient of a Stanford Graduate Fellowship, Siebel Scholar Fellowship, and an NSF CAREER award.
Presenter: Pramod Khargonekar (Homepage)
Friday November 7, 2014 from 11:00am to 12:00pm
The main goal of this talk is to share thoughts and perspectives on key
opportunities and challenges in engineering research; education and
and innovation ecosystem as seen from my perspective at the National
Science Foundation. I will begin with a discussion of the overall
context, drivers, and trends that are driving our strategies.
Pramod Khargonekar received B. Tech. Degree in electrical engineering from the Indian Institute of Technology, Bombay, India, in 1977, and M.S. degree in mathematics and Ph.D. degree in electrical engineering from the University of Florida in 1980 and 1981, respectively.
Khargonekar was an assistant professor of electrical and computer engineering at the University of Florida from 1981 to 1984; associate professor from 1984 to 1988 and professor from 1988 to 1989 of electrical engineering at the University of Minnesota; and professor of electrical engineering and computer science from 1989 to 2001 at The University of Michigan. He was Chairman of the Department of Electrical Engineering and Computer Science from 1997 to 2001 and also held the position of Claude E. Shannon Professor of Engineering Science at The University of Michigan. From 2001 to 2009, he was Dean of the College of Engineering at the University of Florida. He has held the Eckis Professorship in Electrical and Computer Engineering the University of Florida from 2001. He served as Deputy Director of Technology at ARPA-E, U. S. Department of Energy in 2012-13. From March 2013, he has been serving as Assistant Director of the U. S. National Science Foundation leading its Engineering Directorate.
Khargonekar’s research and teaching interests are centered on theory and applications of systems and control. His early work was on mathematical control theory, specifically focusing on robust and H-infinity control analysis and design. During the 1990’s, he was involved in a large multidisciplinary project on applications of control and estimation techniques to semiconductor manufacturing. His current research and teaching interests include systems and control theory, machine learning, and applications to smart electric grid and neural engineering.
Presenter: Kostas Bekris (Homepage)
Friday October 31, 2014 from 11:00am to 12:00pm
Motion planning has progressed over the last couple of decades in
addressing complex challenges in robotics. An important milestone
was the development of practical sampling-based solutions, for
which recently the conditions that allow these methods to achieve
asymptotic optimality have been identified. Based on the
state-of-the-art, this talk will highlight a series of recent
foundational contributions by our research group in this area:
Kostas Bekris is an Assistant Professor of Computer Science at Rutgers University since 2012 and a member of the CBIM center. He received his BS in Computer Science at the University of Crete, Greece and completed his MS and PhD degrees in Computer Science at Rice University, Houston, TX, under the supervision of Prof. Lydia Kavraki. Between 2008 and 2012 he was an Assistant Professor at the University of Nevada, Reno. He works in robotics and his interests include motion planning, especially for systems with dynamics, manipulation, online replanning, motion coordination, as well as applications in cyber-physical systems and simulations. His research group is supported by the National Science Foundation, the National Aeronautics and Space Administration, the Department of Homeland Security and the Department of Defense. His research group is also affiliated with DIMACS and the DHS Center of Excellence CCICADA.
Presenter: Sonia Chernova (Homepage)
Friday October 24, 2014 from 11:00am to 12:00pm
Recent innovations in crowd computing, crowdsourcing and remote access
technologies have altered the way in which many traditional artificial
intelligence and robotics studies are designed, conducted and
evaluated. Research on shared autonomy, human-robot interaction and
robot learning has particularly benefited from the greater access to
data and users that such techniques enable, leading to new data-driven
techniques and more extensive evaluations. In this talk, I will present
ongoing projects aimed at enabling robots to learn from everyday
people, examining ho
Sonia Chernova is an Assistant Professor of Computer Science and Robotics Engineering at Worcester Polytechnic Institute and the director of the Robot Autonomy and Interactive Learning (RAIL) lab. She earned B.S. and Ph.D. degrees in Computer Science from Carnegie Mellon University in 2003 and 2009, and was a Postdoctoral Associate at the MIT Media Lab prior to joining WPI. Her research is focused on interactive machine learning, adjustable autonomy, crowdsourcing and human-robot interaction. This work is supported in part by NSF CAREER, NRI and ONR YIP awards.
Presenter: Torsten Kroeger (Homepage)
Friday October 17, 2014 from 11:00am to 12:00pm
Online and instantaneous robot motion generation is an important feature for
robot motion control systems to let robots respond instantaneously to
unforeseen events. An algorithmic concept that enables instantaneous changes
from sensor-guided robot motion control (e.g., force/torque or visual servo
control) to trajectory-following motion control, and vice versa, will be
presented. The resulting class of on-line trajectory generation algorithms
serves as an intermediate layer between low-level motion control and
high-level sensor-based motion planning.
Dr. Torsten Kroeger is a roboticist at Google and a visiting scholar at Stanford University. He received his Ph.D. in computer science from TU Braunschweig in Germany in 2009 (summa cum laude). In 2010, he joined the Stanford AI Lab as a lecturer and research associate. His research interests are focused on real-time trajectory generation and control of robotic systems. Application domains of his research include industrial robotics and automation, machine tools, haptic devices, surgical robotics, service robotics, mobile robotics, and space telescope control. He has been working as a research consultant for Volkswagen AG, KUKA Roboter GmbH, Manz Automation AG, Auris Surgical Robotics, Inc., and Redwood Robotics, Inc.. He is the founder of Reflexxes GmbH, a startup working on research and development of real-time motion generation software. In 2014, Reflexxes was acquired by Google, where Torsten now leads parts of R&D efforts in the domain of robotics. Torsten is an editor or an associate editor of multiple IEEE conference proceedings, books, and book series. He received the 2014 IEEE RAS Early Career Award, the 2011 Heinrich Buessing Award, the 2011 GFFT Award, two fellowships of the German Research Association, and he was a finalist of the 2012 IEEE/IFR IERA Award and the 2012 euRobotics TechTransfer Award.
Presenter: Timothy Bretl (Homepage)
Friday October 3, 2014 from 11:00am to 12:00pm
My talk in the language of robotics:
"I will show how to establish an appropriate configuration space for robotic manipulation of canonical 'deformable linear objects' like a Kirchhoff elastic rod (e.g., a flexible wire). This result leads to simple algorithms for manipulation and perception that are easy to implement and that work well in practice."
My talk in the language of mathematics:
Timothy Bretl received his B.S. in Engineering and B.A. in Mathematics from Swarthmore College in 1999, and his M.S. in 2000 and Ph.D. in 2005 both in Aeronautics and Astronautics from Stanford University. Subsequently, he was a Postdoctoral Fellow in the Department of Computer Science, also at Stanford University. Since 2006, he has been with the University of Illinois at Urbana-Champaign, where he is an Assistant Professor of Aerospace Engineering and a Research Assistant Professor in the Coordinated Science Laboratory. He received the National Science Foundation Faculty Early Career Development Award in 2010.
Presenter: Steve LaValle (Homepage)
Friday September 26, 2014 from 11:00am to 12:00pm
Using the latest technology, we can safely hijack your most trusted
senses, thereby fooling your brain into believing you are in another
world. Virtual reality (VR) has been around for a long time, but due
to the recent convergence of sensing, display, and computation
technologies, there is an unprecedented opportunity to explore this
form of human augmentation with lightweight, low-cost materials and
simple software platforms.
Steve LaValle started working with Oculus VR in September 2012, a few days after their successful Kickstarter campaign, and was the head scientist up until the Facebook acquisition in March 2014. He developed perceptually tuned head tracking methods based on IMUs and computer vision. He also led a team of perceptual psychologists to provide principled approaches to virtual reality system calibration and the design of comfortable user experiences. In addition to his continuing work at Oculus, he is also Professor of Computer Science at the University of Illinois, where he joined in 2001. He has worked in robotics for over 20 years and is known for his introduction of the Rapidly exploring Random Tree (RRT) algorithm of motion planning and his 2006 book, Planning Algorithms.