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Learning visuomotor policies for agile quadrotor flight presents significant difficulties, primarily from inefficient policy exploration caused by high-dimensional visual inputs and the need for precise and low-latency control.
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Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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End-to-end training of deep visuomotor policies
S. Levine, C. Finn, T. Darrell, and P. Abbeel · 2016
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Temporal convolutional networks: A unified approach to action segmentation
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K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Differential flatness of quadrotor dynamics subject to rotor drag for accurate tracking of high-speed trajectories
M. Faessler, A. Franchi, and D. Scaramuzza · 2017
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Behavioral cloning from observation
F. Torabi, G. Warnell, and P. Stone · 2018
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Reinforcement and imitation learning for diverse visuomotor skills
Y. Zhu, Z. Wang, J. Merel, A. Rusu, T. Erez, S. Cabi, S. Tunyasuvunakool, J. Kramár, R. Hadsell, N. de Freitas, and N. Heess · 2018
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On the continuity of rotation representations in neural networks
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Rma: Rapid motor adaptation for legged robots
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A system for general in-hand object re-orientation
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Legged locomotion in challenging terrains using egocentric vision
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