Predicting Object Dynamics from Visual Images through Active Sensing Experiences
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Predicting Object Dynamics from Visual Images through Active Sensing Experiences •Two phase learning utilizing Recurrent Neural Network with Parametric Bias (RNNPB) and Hierarchical Neural Network •Self-organization of object dynamics with RNNPB •Association of object dynamics with visual images and robot motion using Hierarchical Neural Network Shun Nishide, Tetsuya Ogata, Kazunori Komatani and Hiroshi G. Okuno Dept. of Intelligence Science and Technology, Kyoto University, JAPAN Jun Tani Brain Science Institute, RIKEN, JAPAN Predicting Dynamics of Unknown Object using Robovie II-s
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Predicting Object Dynamics from Visual Images through Active Sensing Experiences. Shun Nishide , Tetsuya Ogata , Kazunori Komatani and Hiroshi G. Okuno Dept. of Intelligence Science and Technology, Kyoto University, JAPAN Jun Tani Brain Science Institute , RIKEN, JAPAN. - PowerPoint PPT Presentation
Transcript of Predicting Object Dynamics from Visual Images through Active Sensing Experiences
Predicting Object Dynamics from Visual Imagesthrough Active Sensing Experiences
• Two phase learning utilizing Recurrent Neural Network with Parametric Bias (RNNPB) and Hierarchical Neural Network
• Self-organization of object dynamics with RNNPB
• Association of object dynamics with visual images and robot motion using Hierarchical Neural Network
• Successful prediction of dynamics for 4 unknown object
Shun Nishide, Tetsuya Ogata, Kazunori Komataniand Hiroshi G. Okuno
Dept. of Intelligence Science and Technology, Kyoto University, JAPAN
Jun Tani Brain Science Institute, RIKEN, JAPAN
Predicting Dynamics of Unknown Object using Robovie II-s
