Teknologiseminar
From Robin
(Difference between revisions)
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| 24.03 || Finishing master thesis (Jim/interaction) || ROBIN master students || ROBIN master students | | 24.03 || Finishing master thesis (Jim/interaction) || ROBIN master students || ROBIN master students | ||
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- | | 21.04 || | + | | 21.04 || Charles – Music Tech in EPEC || Jim about ROBIN projects || Kristian – New ROBIN faculty in 50% shared with music dep |
|- | |- | ||
| 19.05 || Kai Olav – multiple internal models || Tønnes – Real world robot evolution || Jørgen – Robot simulation | | 19.05 || Kai Olav – multiple internal models || Tønnes – Real world robot evolution || Jørgen – Robot simulation |
Revision as of 06:34, 20 April 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 – Fridays 12:45-14:00
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/interaction) | ROBIN master students | ROBIN master students |
21.04 | Charles – Music Tech in EPEC | Jim about ROBIN projects | Kristian – New ROBIN faculty in 50% shared with music dep |
19.05 | Kai Olav – multiple internal models | Tønnes – Real world robot evolution | Jørgen – Robot simulation |
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