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Deep reinforcement learning algorithms require large and diverse datasets in order to learn successful policies for perception-based mobile navigation.
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Learning monocular reactive uav control in cluttered natural environments
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Intention-net: Integrating planning and deep learning for goal-directed autonomous navigation
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
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S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen · 2018
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RoboNet: Large-scale multi-robot learning
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Neural Autonomous Navigation with Riemannian Motion Policy
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Driving policy transfer via modularity and abstraction
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Data-efficient hierarchical reinforcement learning
O. Nachum, S. S. Gu, H. Lee, and S. Levine · 2018
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Neural network dynamics for model-based deep reinforcement learning with model-free fine-tuning
A. Nagabandi, G. Kahn, R. S. Fearing, and S. Levine · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
K. Chua, R. Calandra, R. McAllister, and S. Levine · 2018
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Beauty and the beast: Optimal methods meet learning for drone racing
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Combining optimal control and learning for visual navigation in novel environments
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X. Meng, N. Ratliff, Y. Xiang, and D. Fox · 2019
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Learning navigation behaviors end-to-end with autorl
H.-T. L. Chiang, A. Faust, M. Fiser, and A. Francis · 2019
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VR-goggles for robots: Real-to-sim domain adaptation for visual control
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X. Meng, N. Ratliff, Y. Xiang, and D. Fox · 2019
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D. Hejna, P. Abbeel, and L. Pinto · 2020
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Compositional Transfer in Hierarchical Reinforcement Learning
M. Wulfmeier, A. Abdolmaleki, R. Hafner, J. T. Springenberg, M. Neunert, T. Hertweck, T. Lampe, N. Siegel, N. Heess, and M. Riedmiller · 2020
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BADGR: An autonomous self-supervised learning-based navigation system
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