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In this work, we present an approach to learn cost maps for driving in complex urban environments from a very large number of demonstrations of driving behaviour by human experts.
Neural network based autonomous navigation
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Pieter Abbeel and Andrew Y Ng · 2004
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Principles of robot motion: theory, algorithms, and implementation
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Regularization and variable selection via the elastic net
Hui Zou and Trevor Hastie · 2005
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Autonomous driving in urban environments: Boss and the urban challenge
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Junior: The stanford entry in the urban challenge
Michael Montemerlo, Jan Becker, Suhrid Bhat, Hendrik Dahlkamp, Dmitri Dolgov, Scott Ettinger, Dirk Haehnel, Tim Hilden, Gabe Hoffmann, Burkhard Huhnke, et al · 2008
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Maximum entropy inverse reinforcement learning
Brian D Ziebart, Andrew L Maas, J Andrew Bagnell, and Anind K Dey · 2008
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Apprenticeship learning for motion planning with application to parking lot navigation
P. Abbeel, Dmitri Dolgov, A.Y. Ng, and S. Thrun · 2008
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A survey of robot learning from demonstration
Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning · 2009
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Learning to search: Functional gradient techniques for imitation learning
Nathan D. Ratliff, David Silver, and J. Andrew Bagnell · 2009
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey J Gordon, and J Andrew Bagnell · 2010
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Nonlinear inverse reinforcement learning with gaussian processes
Sergey Levine, Zoran Popovic, and Vladlen Koltun · 2011
Planit: A crowdsourcing approach for learning to plan paths from large scale preference feedback
Ashesh Jain, Debarghya Das, Jayesh K Gupta, and Ashutosh Saxena · 2015
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Deepdriving: Learning affordance for direct perception in autonomous driving
Chenyi Chen, Ari Seff, Alain Kornhauser, and Jianxiong Xiao · 2015
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Learning driving styles for autonomous vehicles from demonstration
Markus Kuderer, Shilpa Gulati, and Wolfram Burgard · 2015
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Fully convolutional networks for semantic segmentation
Jonathan Long, Evan Shelhamer, and Trevor Darrell · 2015
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Inverse reinforcement learning with locally consistent reward functions
Quoc Phong Nguyen, Bryan Kian Hsiang Low, and Patrick Jaillet · 2015
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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Continually improving large scale long term visual navigation of a vehicle in dynamic urban environments
Winston Churchill and Paul Newman · 2012
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Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2012
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Activity Forecasting
Kris M. Kitani, Brian D. Ziebart, James Andrew Bagnell, and Martial Hebert · 2012
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Deep inverse reinforcement learning
Markus Wulfmeier, Peter Ondruska, and Ingmar Posner · 2015
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A multi-range architecture for collision-free off-road robot navigation
Pierre Sermanet, Raia Hadsell Marco Scoffier, Matt Grimes, Jan Ben, Ayse Erkan, Chris Crudele, Urs Muller, and Yann Lecun
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Inverse Reinforcement Learning from Failure
Kyriacos Shiarlis, Joao Messias, Maarten van Someren, and Shimon Whiteson · 2015
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Socially compliant mobile robot navigation via inverse reinforcement learning
Henrik Kretzschmar, Markus Spies, Christoph Sprunk, and Wolfram Burgard · 2016
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Guided cost learning: Deep inverse optimal control via policy optimization
Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
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