Unsupervised Learning for Physical Interaction through Video Prediction Unsupervised Learning for Physical Interaction through Video Prediction
Paper summary Problem ---------- Given a video of robot motion, predict future frames of the motion. Dataset ----------- 1. The authors assembled a new dataset of 59,000 robot interactions involving pushing motions. 2. Human3.6m - video, depth and mocap. action include: sitting, purchasing, waiting... Approach ------------ * Use LSTMs to "remember" previous frames. * Predict 10 transformations from previous frame (each approach represents the transformation differently). * Predict a mask to determine which transformation is applied to which pixel. The authors suggest 3 models based on this approach: 1. Dynamic Neural Advection 2. Convolutional Dynamic Neural Advection 3. Spatial Transformer Predictors
arxiv.org
scholar.google.com
Unsupervised Learning for Physical Interaction through Video Prediction
Finn, Chelsea and Goodfellow, Ian J. and Levine, Sergey
arXiv e-Print archive - 2016 via Local Bibsonomy
Keywords: dblp


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