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The goal of the MPLab is to develop systems that perceive and interact with humans in real time using natural communication channels. To this effect we are developing perceptual primitives to detect and track human faces and to recognize facial expressions. We are also developing algorithms for robots that develop and learn to interact with people on their own. Applications include personal robots, perceptive tutoring systems, and system for clinical assessment, monitoring, and intervention.

  • Introduction to the MPLab (PDF)
  • MPLAB 5 Year Progress Report (PDF)

  • NEWS


    BEHAVIOR:
    Robot Cockroach Tests Insect Decision-Making Behavior Elizabeth Pennisi

    Robot cockroaches coated with pheromones are so well accepted by the
    household pests that the robots become part of the insects’ collective
    decision-making process, researchers report on page 1155 of this week’s
    issue of Science. (For more on robotics, see this week’s special section.)

    Full story at http://www.sciencemag.org/cgi/content/full/318/5853/1055?etoc

    Below is the link to the Nov 6 2007, NPR’s Morning Edition Story about our PNAS Article.

  • http://www.npr.org/templates/story/story.php?storyId=16035299&sc=emaf

    Slashdot linked to a New Scientist article about Javier and Boom’s recent PNAS publication. Great job guys!  Here is the New Scientist link:

    http://technology.newscientist.com/article/dn12879-giggling-robot-becomes-one-of-the-kids-.html

    Original slashdot thread:

    http://hardware.slashdot.org/article.pl?sid=07/11/06/2018230

    NPR Morning Edition, November 6, 2007 Story on the RUBI Project

    The Center for Human Development

    Distinguished Speaker Series

    Presents a talk by:

    Elizabeth M. Brannon, Duke University

    Friday, November 9, 2007

    3:00-5:00pm

    AP&M

    Room 4301

    Evolutionary and Developmental Precursors to a Concept of Number

    Adult humans quantify, label, and categorize almost every aspect of the world with numbers. The ability to use numbers is one of the most complex cognitive abilities that humans possess and is often held up as a defining feature of the human mind. In my talk I will present a body of data that demonstrates that there are strong developmental and evolutionary precursors to adult mathematical cognition that can be seen by studying human infants and nonhuman primates. I will demonstrate the similarities in the psychophysics of numerical discrimination in adults, monkeys, and human infants, explore the relationship between the representation of number and continuous variables, and experimentally illustrate the amodal and abstract nature of nonverbal number representation. Further I will describe research using ERPs with infants, fMRI with children, and single-unit recordings with monkeys that suggests that the neural bases of numerical cognition are similar throughout development and likely to be evolutionarily ancient.

    TALK, Wednesday, November 7, 3 – 4:30 PM, EBU3-4140

    Subspectral Algorithms for Sparse Learning, Optimization & Inference

    Baback Moghaddam
    Jet Propulsion Laboratory
    California Institute of Technology

    I will present a general class of “subspectral” algorithms (sparse
    eigenvector techniques) for solving NP-hard combinatorial optimization
    problems in three applied domains: (Un)Supervised Learning (e.g. PCA &
    LDA), Quadratic/Entropic Optimization (e.g. Least-Squares & MaxEnt)
    and 3) Bayesian Inference (e.g. Automatic Relevance Determination).
    Efficient algorithms for both optimal and approximate greedy solutions
    are derived using analytic eigenvalue bounds. Sample applications
    presented include “sparse PCA” for variable selection (in statistics),
    “sparse LDA” for classification (gene discovery), sparse kernel
    regression (robotics & control), sparse quadratic programming
    (portfolio optimization), graph model selection (sensor networks) and
    sparse Bayesian inference for computer vision (face recognition &
    OCR).

    *Biography:* Baback Moghaddam joined the Jet Propulsion Laboratory in 2007
    as a Principal Member, with the Machine Learning and Instrument Autonomy Group.
    Prior to JPL he was a Senior Research Scientist at the Mitsubishi Electric Research
    Laboratory (MERL) since 1997. He received his Ph.D. in Electrical Engineering and
    Computer Science from the Massachusetts Institute of Technology in 1997, where
    he was a member of the Vision & Modeling Group of the MIT Media Laboratory
    since 1992. As part of his doctoral work at MIT he developed an automatic face
    recognition system which won the 1996 DARPA “FERET” Face Recognition
    competition. He has authored numerous articles on 2D face recognition and 3D
    facial modeling in leading journals and conferences, including the core chapter in
    Springer-Verlag’s Biometric Series, The Handbook of Face Recognition.

      Here is a link to info about Omron’s smile detection system. Note it used 10,000 images for training the detector. http://www.technologyreview.com/Infotech/19620/?a=f

      Here is a game that teaches some subtle phonemic distinctions in Finnish. Your goal is to learn the name of these 4 popular Finnish characters. When you hear the name click on the corresponding character. To complete the game you most get 16 consecutive correct responses.  Finnish Game


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