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In dynamic environments, learned controllers are supposed to take motion into account when selecting the action to be taken.
An adaptive robotic tracking system using optical flow
R. C. Luo, R. E. Mullen, and D. E. Wessell · 1988
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Real-time visual servoing
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A biomimetic reactive navigation system using the optical flow for a rotary-wing UAV in urban environment
L. Muratet, S. Doncieux, and J.-A. Meyer · 2004
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Biologically plausible visual homing methods based on optical flow techniques
A. Vardy and R. Moller · 2005
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K. Souhila and A. Karim · 2007
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Mujoco: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
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A survey of optical flow techniques for robotics navigation applications
H. Chao, Y. Gu, and M. Napolitano · 2014
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Flownet: Learning optical flow with convolutional networks
A. Dosovitskiy, P. Fischer, E. Ilg, P. Häusser, C. Hazırbaş, V. Golkov, P. v.d. Smagt, D. Cremers, and T. Brox · 2015
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, et al · 2015
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G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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Unsupervised learning for physical interaction through video prediction
C. Finn, I. J. Goodfellow, and S. Levine · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2016
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Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
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Catching a flying ball with a vision-based quadrotor
K. Su and S. Shen · 2016
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Temporal segment networks: Towards good practices for deep action recognition
L. Wang, Y. Xiong, Z. Wang, Y. Qiao, D. Lin, X. Tang, and L. Van Gool · 2016
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Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness
J. J. Yu, A. W. Harley, and K. G. Derpanis · 2016
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Hindsight experience replay
Flownet 2.0: Evolution of optical flow estimation with deep networks
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox · 2017
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Reinforcement learning with unsupervised auxiliary tasks
M. Jaderberg, V. Mnih, W. M. Czarnecki, T. Schaul, J. Z. Leibo, D. Silver, and K. Kavukcuoglu · 2017
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Learning to navigate in complex environments
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MINOS: Multimodal indoor simulator for navigation in complex environments
M. Savva, A. X. Chang, A. Dosovitskiy, T. Funkhouser, and V. Koltun · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, O. Pieter Abbeel, and W. Zaremba · 2017
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Policy transfer via modularity and reward guiding
I. Clavera, D. Held, and P. Abbeel · 2017
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Learning to act by predicting the future
A. Dosovitskiy and V. Koltun · 2017
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Self-supervised visual planning with temporal skip connections
F. Ebert, C. Finn, A. X. Lee, and S. Levine · 2017
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Deep visual foresight for planning robot motion
C. Finn and S. Levine · 2017
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
S. Gu, E. Holly, T. Lillicrap, and S. Levine · 2017
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Deep reinforcement learning that matters
P. Henderson, R. Islam, P. Bachman, J. Pineau, D. Precup, and D. Meger · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
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End-to-end learning of driving models from large-scale video datasets
H. Xu, Y. Gao, F. Yu, and T. Darrell · 2017
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Learning actionable representations from visual observations
D. Dwibedi, J. Tompson, C. Lynch, and P. Sermanet · 2018
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Unsupervised video object segmentation for deep reinforcement learning
V. Goel, J. Weng, and P. Poupart · 2018
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Driving policy transfer via modularity and abstraction
M. Müller, A. Dosovitskiy, B. Ghanem, and V. Koltun · 2018
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Y. Tassa, Y. Doron, A. Muldal, T. Erez, Y. Li, D. d. L. Casas, D. Budden, A. Abdolmaleki, J. Merel, A. Lefrancq, et al · 2018
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