SE3-nets: Learning rigid body motion using deep neural networks
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[1] S Ullman,et al. Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.
[2] R. Baillargeon. Infants' Physical World , 2004 .
[3] E. Todorov,et al. A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems , 2005, Proceedings of the 2005, American Control Conference, 2005..
[4] Marc Toussaint,et al. Robot trajectory optimization using approximate inference , 2009, ICML '09.
[5] Geoffrey E. Hinton,et al. Transforming Auto-Encoders , 2011, ICANN.
[6] Clément Farabet,et al. Torch7: A Matlab-like Environment for Machine Learning , 2011, NIPS 2011.
[7] Carl E. Rasmussen,et al. PILCO: A Model-Based and Data-Efficient Approach to Policy Search , 2011, ICML.
[8] Vincent Lepetit,et al. Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes , 2012, ACCV.
[9] Vincent Lepetit,et al. Model Based Training, Detection and Pose Estimation of Texture-Less 3D Objects in Heavily Cluttered Scenes , 2012, ACCV.
[10] Yuval Tassa,et al. MuJoCo: A physics engine for model-based control , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[11] Jessica B. Hamrick,et al. Simulation as an engine of physical scene understanding , 2013, Proceedings of the National Academy of Sciences.
[12] Vikash K. Mansinghka,et al. Reconciling intuitive physics and Newtonian mechanics for colliding objects. , 2013, Psychological review.
[13] Byron Boots,et al. Learning predictive models of a depth camera & manipulator from raw execution traces , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).
[14] Thomas Brox,et al. Dense Semi-rigid Scene Flow Estimation from RGBD Images , 2014, ECCV.
[15] Dieter Fox,et al. DART: Dense Articulated Real-Time Tracking , 2014, Robotics: Science and Systems.
[16] Sofiane Achiche,et al. From Inverse Kinematics to Optimal Control , 2014 .
[17] Thomas B. Schön,et al. From Pixels to Torques: Policy Learning with Deep Dynamical Models , 2015, ICML 2015.
[18] Scott E. Reed,et al. Weakly-supervised Disentangling with Recurrent Transformations for 3D View Synthesis , 2015, NIPS.
[19] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[20] Yoshua Bengio,et al. Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.
[21] Joshua B. Tenenbaum,et al. Deep Convolutional Inverse Graphics Network , 2015, NIPS.
[22] Sergey Levine,et al. Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders , 2015, ArXiv.
[23] Koray Kavukcuoglu,et al. Multiple Object Recognition with Visual Attention , 2014, ICLR.
[24] Alex Graves,et al. DRAW: A Recurrent Neural Network For Image Generation , 2015, ICML.
[25] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[26] Martin A. Riedmiller,et al. Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images , 2015, NIPS.
[27] Zoran Popovic,et al. Interactive Control of Diverse Complex Characters with Neural Networks , 2015, NIPS.
[28] Jitendra Malik,et al. Learning to See by Moving , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[29] Dieter Fox,et al. DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[30] Honglak Lee,et al. Action-Conditional Video Prediction using Deep Networks in Atari Games , 2015, NIPS.
[31] Bruno A. Olshausen,et al. Discovering Hidden Factors of Variation in Deep Networks , 2014, ICLR.
[32] Andrew Zisserman,et al. Spatial Transformer Networks , 2015, NIPS.
[33] Viorica Patraucean,et al. gvnn: Neural Network Library for Geometric Computer Vision , 2016, ECCV Workshops.
[34] Jitendra Malik,et al. Learning Visual Predictive Models of Physics for Playing Billiards , 2015, ICLR.
[35] Ali Farhadi,et al. Newtonian Image Understanding: Unfolding the Dynamics of Objects in Static Images , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] William F. Whitney. Disentangled Representations in Neural Models , 2016, ArXiv.
[37] Jitendra Malik,et al. Learning to Poke by Poking: Experiential Learning of Intuitive Physics , 2016, NIPS.
[38] Mario Fritz,et al. To Fall Or Not To Fall: A Visual Approach to Physical Stability Prediction , 2016, ArXiv.
[39] Rob Fergus,et al. Learning Physical Intuition of Block Towers by Example , 2016, ICML.
[40] J. Andrew Bagnell,et al. A convex polynomial force-motion model for planar sliding: Identification and application , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[41] Sergey Levine,et al. Deep spatial autoencoders for visuomotor learning , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[42] Ali Farhadi,et al. "What Happens If..." Learning to Predict the Effect of Forces in Images , 2016, ECCV.
[43] Sergey Levine,et al. Unsupervised Learning for Physical Interaction through Video Prediction , 2016, NIPS.
[44] Oliver Brock,et al. Interactive Perception: Leveraging Action in Perception and Perception in Action , 2016, IEEE Transactions on Robotics.
[45] Trevor Darrell,et al. Fully Convolutional Networks for Semantic Segmentation , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[46] Rustam Stolkin,et al. Learning modular and transferable forward models of the motions of push manipulated objects , 2017, Auton. Robots.