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VERSION:2.0
X-WR-CALNAME:Robotics Seminar Series Fall 2008 Events
BEGIN:VEVENT
DTSTART;TZID=US/Eastern:20081202T160000
DTEND;TZID=US/Eastern:20081202T170000
URL;VALUE=URI:http://www.csail.mit.edu/events/eventcalendar/calendar.php?show=event&id=2048
SUMMARY:Learning in Human-Robot Teams
LOCATION:32-D463 (Stata Center - Star Conference Room)
DESCRIPTION:Series: Robotics Seminar Series Fall 2008\nSpeaker:  Chad Jenkins\, Brown University\nHost: Nicholas Roy\, MIT CSAIL\nContact: Marcia Davidson\, 617-253-5817\, marcia@csail.mit.edu\nRefreshment Time: 3:45PM\nRelevant URL: <a href=""></a>\nA principal goal of robotics is to realize embodied systems that are effective collaborators in human endeavors pursued in the physical world. Human-robot collaborations can occur in a variety of forms\, including autonomous robotic assistants\, mixed-initiative robot explorers\, and augmentations of the human body. For these collaborations to be effective\, human users must have the ability to realize their intended behavior into actual robot control policies. At run-time\, robots should be able to manipulate an environment and engage in two-way communication in a manner suitable to their human users. Further\, the tools for programming\, communicating with\, and manipulating using robots should be accessible to the diverse sets of technical abilities present in society.  Towards the goal of effective human-robot collaboration\, learning from demonstration (LfD) has emerged as a central theme of our work for the natural instruction of autonomous robots by human users. In robot LfD\, desired cognitive functions for a robot (perception\, decision making\, or motion control) are implicit in human demonstration rather than explicitly coded in a computer program.\n\nIn this talk\, I will present our work into learning priors from human demonstration for robot perception and control using manifold-based dimension reduction.  My specific focus will be the development and application of manifold learning algorithms to estimate subspace priors for spatial and time-series data generated by humans.  I will discuss our approach to spatio-temporal dimension reduction in the context of manifold learning.  Using manifold learning\, results will be presented from learning priors for: 1) classifying tactile signatures to recognize successful grasps on the NASA Robonaut\, 2) providing low-dimensional control spaces for neural prosthetics\, 3) learning motion primitives from human movement data\, and 4) extracting kinematic models and poses from multi-view video.  Our approach to learning priors will be cast in our broader context for policy learning and computational models for communication in multi-robot multi-human systems.\n\nOdest Chadwicke Jenkins\, Ph.D.\, is an Assistant Professor of Computer Science at Brown University. Prof. Jenkins earned his B.S. in Computer Science and Mathematics at Alma College (1996)\, M.S. in Computer Science at Georgia Tech (1998)\, and Ph.D. in Computer Science at the University of Southern California (2003). In 2007\, he received Young Investigator funding from the Office of Naval Research and the Presidential Early Career Award for Scientists and Engineers (PECASE) for his work in learning primitive models of human motion for humanoid robot control and kinematic tracking.
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