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Deep networks trained on demonstrations of human driving have learned to follow roads and avoid obstacles.
ALVINN: An autonomous land vehicle in a neural network
D. Pomerleau · 1988
Earlier work this paper cites.
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
R. S. Sutton, D. Precup, and S. P. Singh · 1999
Earlier work this paper cites.
Recent advances in hierarchical reinforcement learning
A. G. Barto and S. Mahadevan · 2003
Earlier work this paper cites.
Off-road obstacle avoidance through end-to-end learning
Y. LeCun, U. Muller, J. Ben, E. Cosatto, and B. Flepp · 2005
Earlier work this paper cites.
Imitation learning for locomotion and manipulation
N. D. Ratliff, J. A. Bagnell, and S. S. Srinivasa · 2007
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
B. D. Ziebart, A. L. Maas, J. A. Bagnell, and A. K. Dey · 2008
Earlier work this paper cites.
Navigate like a cabbie: Probabilistic reasoning from observed context-aware behavior
B. D. Ziebart, A. L. Maas, A. K. Dey, and J. A. Bagnell · 2008
Earlier work this paper cites.
A survey of robot learning from demonstration
B. Argall, S. Chernova, M. M. Veloso, and B. Browning · 2009
Earlier work this paper cites.
Learning and generalization of motor skills by learning from demonstration
P. Pastor, H. Hoffmann, T. Asfour, and S. Schaal · 2009
Earlier work this paper cites.
Autonomous helicopter aerobatics through apprenticeship learning
P. Abbeel, A. Coates, and A. Y. Ng · 2010
Earlier work this paper cites.
Learning from demonstration for autonomous navigation in complex unstructured terrain
D. Silver, J. A. Bagnell, and A. Stentz · 2010
Earlier work this paper cites.
A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G. J. Gordon, and J. A. Bagnell · 2011
Earlier work this paper cites.
Understanding natural language commands for robotic navigation and mobile manipulation
S. Tellex, T. Kollar, S. Dickerson, M. R. Walter, A. G. Banerjee, S. J. Teller, and N. Roy · 2011
Earlier work this paper cites.
Learning parameterized skills
B. C. da Silva, G. Konidaris, and A. G. Barto · 2012
Cited alongside, same era.
Reinforcement learning to adjust parametrized motor primitives to new situations
J. Kober, A. Wilhelm, E. Oztop, and J. Peters · 2012
Cited alongside, same era.
Robot learning from demonstration by constructing skill trees
G. Konidaris, S. Kuindersma, R. A. Grupen, and A. G. Barto · 2012
Cited alongside, same era.
Model-based imitation learning by probabilistic trajectory matching
P. Englert, A. Paraschos, J. Peters, and M. P. Deisenroth · 2013
Cited alongside, same era.
Reinforcement learning in robotics: A survey
J. Kober, J. A. Bagnell, and J. Peters · 2013
Cited alongside, same era.
Guided policy search
S. Levine and V. Koltun · 2013
Cited alongside, same era.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Later among the works it cites.
Universal value function approximators
T. Schaul, D. Horgan, K. Gregor, and D. Silver · 2015
Later among the works it cites.
End to end learning for self-driving cars
M. Bojarski, D. D. Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba · 2016
Later among the works it cites.
A machine learning approach to visual perception of forest trails for mobile robots
A. Giusti, J. Guzzi, D. Ciresan, F.-L. He, J. P. Rodriguez, F. Fontana, M. Faessler, C. Forster, J. Schmidhuber, G. Di Caro, D. Scaramuzza, and L. Gambardella · 2016
Later among the works it cites.
Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation
T. D. Kulkarni, K. Narasimhan, A. Saeedi, and J. B. Tenenbaum · 2016
Later among the works it cites.
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Learning semantic maps from natural language descriptions
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Multi-task policy search for robotics
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Cited alongside, same era.
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Later among the works it cites.
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CARLA: An open urban driving simulator
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