Select a calendar:
Filter January Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Events for January 05, 2009
-
Aviation Safety Management Systems - Jan.05-16, 2008
Mon, Jan 05, 2009
Aviation Safety and Security Program
Conferences, Lectures, & Seminars
ASMS 09-2
For more information and to register for Aviation Safety and Security Program courses, please visit http://viterbi.usc.edu/aviation.
Audiences: Registered Audiences Only
Contact: Viterbi Professional Programs
-
Meet USC
Mon, Jan 05, 2009
Viterbi School of Engineering Undergraduate Admission
Workshops & Infosessions
This half day program is designed for prospective freshmen and family members. Meet USC includes an information session on the University and the Admission process; a student led walking tour of campus and a meeting with us in the Viterbi School. Meet USC is designed to answer all of your questions about USC, the application process and financial aid.Reservations are required for Meet USC. This program occurs twice, once at 9:00 a.m. and again at 1:00 p.m. Please visit http://www.usc.edu/admission/undergraduate/visit/meet_usc.html to check availability and make an appointment. Be sure to list an Engineering major as your "intended major" on the webform!
Location: USC Admission Center
Audiences: Prospective Freshmen and Family Members - RESERVATIONS REQUIRED
Contact: Viterbi Admission
-
Manifold Learning in Human-Robot Teams
Mon, Jan 05, 2009 @ 02:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Prof. Chad Jenkins, Brown University
Host: Prof. Maja MataricAbstract:
A 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.In 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.Biography:
Odest 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). He is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) for his work in physics-based human tracking. He has also received Young Investigator awards from the Office of Naval Research (ONR) for his research in learning dynamical primitives from human motion and the Air Force Office of Scientific Research (AFOSR) for his work in manifold learning and multi-robot coordination. His research addresses problems in robot learning and human-robot interaction, primarily focused on robot learning from demonstration, as well as topics in computer vision, machine learning, and computer animation.Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: CS Colloquia
