Teknologiseminar

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|  10.03    || Sichao Song  || Zia – sensing in MECS ||  Weria – robot control in MECS
|  10.03    || Sichao Song  || Zia – sensing in MECS ||  Weria – robot control in MECS
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|-  
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|  24.03    || ROBIN master students || ROBIN master students ||
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|  24.03    || Finishing master thesis (Jim) || ROBIN master students || ROBIN master students
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|-  
|  21.04    || Jim about ROBIN projects  ||  Charles – Music Tech in EPEC || Kristian – New ROBIN faculty in 50% shared with music dep
|  21.04    || Jim about ROBIN projects  ||  Charles – Music Tech in EPEC || Kristian – New ROBIN faculty in 50% shared with music dep

Revision as of 13:50, 4 March 2017

Meeting structure

Place: ROBIN pause area, 4th floor Ole-Johan Dahls hus

13:00-13:15 Brief exchange of recent news, plans etc

13:15-14:00 Technology introduction or conference / journal review, see guidelines

14:00-14:30 Research presentation by two people (presentation of and discussion around group member's current research (15 mins each including discussion))

Please upload pdf of your presentation here: http://robinternal.wiki.ifi.uio.no and link to it from the table below.

Dates and subjects - Spring 2017

DON'T EDIT THIS WITH THE RICH EDITOR! IT DESTROYS THE TEXT VERSION!

When Technology / journal review' / guests Own research 1' Own research 2
10.03 Sichao Song Zia – sensing in MECS Weria – robot control in MECS
24.03 Finishing master thesis (Jim) ROBIN master students ROBIN master students
21.04 Jim about ROBIN projects Charles – Music Tech in EPEC Kristian – New ROBIN faculty in 50% shared with music dep
19.05 Tønnes – Real world robot evolution Kai Olav – multiple internal models


Overview of earlier seminars

Examples of interesting topics for technology introduction

  • Robotics
    • SLAM
    • embodied cognition
    • New sensors
    • Flying robotics
  • ANN
    • Recursive NNs
    • learning: supervised/unsupervised/reinforcement
    • spiking networks
    • NEAT / HyperNEAT
    • Deep NNs
  • Evolutionary algorithms
    • Performance comparison of EAs (statistical tests)
    • Differential evolution
    • Diversity preservation methods
    • Novelty search, MAP-Elites and variants
  • Tools
    • R
    • ROS
    • Deep learning tools
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