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Presenter: Jnaneshwar Das (Homepage)
Friday January 17, 2014 from 11:00am to 12:00pm
* Alternate Location: Levine 307 (3330 Walnut Street)*
Robotic sampling is attractive in many field robotics applications that
require persistent collection of physical samples for ex-situ analysis.
Examples abound in the earth sciences in studies involving the
collection of rock, soil, and water samples for lab analysis. The
desirability of samples in these domains can be expressed as a property
that cannot be determined in-situ, but can be predicted by covariates
measurable in real-time using sensors carried aboard a robot.
Jnaneshwar Das is a Ph.D. candidate in Computer Science at the Robotic Embedded Systems Laboratory, University of Southern California. His research interests are in the use of robotic assets for the earth sciences. Since the summer of 2009, he has been collaborating with the Monterey Bay Aquarium Research Institute (MBARI) on prediction of plankton distribution in the coastal ocean from in-situ data and physical water samples gathered by autonomous underwater vehicles (AUVs). Prior to this effort, he designed and deployed the first prototype of an Oceanographic Decision Support System, http://odss.mbari.org/, used actively by scientists to monitor assets during large-scale field campaigns. He received his M.S. in Computer Science from USC in 2008.
Presenter: Jeremy Gillula
Thursday January 16, 2014 from 12:00pm to 1:00pm
* Alternate Location: Moore 317 (inside Moore 316)*
Reinforcement learning has proven itself to be a powerful technique
in robotics, however it has rarely been employed to learn in a
hardware-in-the-loop environment due to the fact that spurious
training data could cause a robot to take an unsafe (and potentially
Jeremy Gillula is a postdoctoral scholar at the University of California at Berkeley. He obtained his masters and doctoral degrees from Stanford University, where his research focused on algorithms for guaranteeing safety for online learning methods applied to robotic systems. Prior to that, he did his undergraduate work at Caltech, where he was a key member of the school's Desert DARPA Grand Challenge team, working on sensing systems and sensor fusion. In addition to robotics, he is also interested in the area of technology policy and law, particularly how to best regulate the burgeoning areas of autonomous ground and aerial vehicles.
Presenter: Joelle Pineau (Homepage)
Friday January 31, 2014 from 11:00am to 12:00pm
A key skill for mobile robots is the ability to navigate efficiently
through their environment. In the case of social or assistive robots, this
involves navigating through human crowds. Typical performance criteria,
such as reaching the goal using the shortest path, are not appropriate in
such environments, where it is more important for the robot to move in a
socially acceptable manner. In this talk I will describe new methods based
on imitation and reinforcement learning which we have developed to allow
robots to achieve socially adaptive path planning in human environments.
Joelle Pineau is an Associate Professor at the School of Computer Science at McGill University, where she co-directs the Reasoning and Learning Lab. She received her PhD in Robotics from Carnegie Mellon University in 2004. Her research centres on developing efficient algorithms for learning and decision-making in partially observable stochastic domains, and applying these algorithms to complex problems in robotics and health-care.
Thursday January 9, 2014
Penn roboticists take on the worldBy Evan Lerner
Thursday December 12, 2013 at 6:00pm
** ROBOCKEY 2013 ***
It’s time for the fifth-annual Robockey tournament!
As this year’s mechatronics class is drawing to a close, many of you have already born witness to the growing flurry of activity around the GM lab.
Sunday November 17, 2013
Presenter: Andrea Thomaz (Homepage)
Friday April 11, 2014 from 11:00am to 12:00pm
In this talk I present recent work from the Socially Intelligent
Machines Lab at Georgia Tech. One of the focuses of our lab is on
Socially Guided Machine Learning, building robot systems that can learn
from everyday human teachers. We look at standard Machine Learning
interactions and redesign interfaces and algorithms to support the
collection of learning input from naive humans.
Andrea L. Thomaz is an Associate Professor of Interactive Computing at the Georgia Insti- tute of Technology. She directs the Socially Intelligent Machines lab, which is affiliated with the Robotics and Intelligent Machines (RIM) Center and with the Graphics Visualization and Usability (GVU) Center. She earned a B.S. in Electrical and Computer Engineering from the University of Texas at Austin in 1999, and Sc.M. and Ph.D. degrees from MIT in 2002 and 2006. Dr. Thomaz has published in the areas of Artificial Intelligence, Robotics, and Human-Robot Interaction. She has received recognition as a young leader in her field, receiving an ONR Young Investigator Award in 2008, and an NSF CAREER award in 2010. Her work has been featured on the front page of the New York Times, on NOVA Science Now, she was named one of MIT Technology Review’s Top 35 under 35 in 2009, and on Popular Science Magazine’s Brilliant 10 list in 2012.
Tuesday November 5, 2013
"A robot learns to ice skate" in the Daily Pennsylvanian
‘RHex,’ started over a decade ago for search-and-rescue, is learning to walk on ice
November 5, 2013, 5:32 pm · Updated November 5, 2013, 8:49 pm·
Tuesday October 22, 2013
Popular Mechanics has given Engineering prof Vijay Kumar, along with his colleagues and students, an annual Breakthrough Award for their work on flying robot swarms!!!
Friday October 25, 2013