Learning Situational Driving
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Eshed Ohn-Bar | Aseem Behl | Andreas Geiger | Aditya Prakash | Kashyap Chitta | Andreas Geiger | Eshed Ohn-Bar | Aseem Behl | Aditya Prakash | Kashyap Chitta
[1] Rahul Sukthankar,et al. Cognitive Mapping and Planning for Visual Navigation , 2017, International Journal of Computer Vision.
[2] Dean Pomerleau,et al. ALVINN, an autonomous land vehicle in a neural network , 2015 .
[3] Yann LeCun,et al. Off-Road Obstacle Avoidance through End-to-End Learning , 2005, NIPS.
[4] Leonidas J. Guibas,et al. Mid-Level Visual Representations Improve Generalization and Sample Efficiency for Learning Active Tasks , 2018, ArXiv.
[5] Leonidas J. Guibas,et al. Taskonomy: Disentangling Task Transfer Learning , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[6] Doina Precup,et al. Intra-Option Learning about Temporally Abstract Actions , 1998, ICML.
[7] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[8] Alexey Dosovitskiy,et al. End-to-End Driving Via Conditional Imitation Learning , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[9] Vladlen Koltun,et al. On Offline Evaluation of Vision-based Driving Models , 2018, ECCV.
[10] Trevor Darrell,et al. Generalized Zero- and Few-Shot Learning via Aligned Variational Autoencoders , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Neil Smith,et al. OIL: Observational Imitation Learning , 2018, Robotics: Science and Systems.
[12] Vladlen Koltun,et al. Does computer vision matter for action? , 2019, Science Robotics.
[13] Anthony M. Zador,et al. A critique of pure learning and what artificial neural networks can learn from animal brains , 2019, Nature Communications.
[14] Vittorio Ferrari,et al. Situational object boundary detection , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Mohit Sharma,et al. Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information , 2018, ICLR.
[16] Eric P. Xing,et al. CIRL: Controllable Imitative Reinforcement Learning for Vision-based Self-driving , 2018, ECCV.
[17] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[18] Geoffrey J. Gordon,et al. A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning , 2010, AISTATS.
[19] Jianxiong Xiao,et al. DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[20] Leonidas J. Guibas,et al. Situational Fusion of Visual Representation for Visual Navigation , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[21] David Q. Mayne,et al. Robust model predictive control of constrained linear systems with bounded disturbances , 2005, Autom..
[22] Yang Gao,et al. End-to-End Learning of Driving Models from Large-Scale Video Datasets , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[23] Christopher Burgess,et al. beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.
[24] Vladlen Koltun,et al. Learning by Cheating , 2019, CoRL.
[25] Charles C. MacAdam,et al. Understanding and Modeling the Human Driver , 2003 .
[26] Nitish Srivastava,et al. Unsupervised Learning of Video Representations using LSTMs , 2015, ICML.
[27] Leslie Pack Kaelbling,et al. Residual Policy Learning , 2018, ArXiv.
[28] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[29] Benjamin Van Roy,et al. Deep Exploration via Bootstrapped DQN , 2016, NIPS.
[30] Sergey Levine,et al. MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies , 2019, NeurIPS.
[31] Nikolaus Hansen,et al. Completely Derandomized Self-Adaptation in Evolution Strategies , 2001, Evolutionary Computation.
[32] Bernard Ghanem,et al. Driving Policy Transfer via Modularity and Abstraction , 2018, CoRL.
[33] Eder Santana,et al. Exploring the Limitations of Behavior Cloning for Autonomous Driving , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[34] Andreas Geiger,et al. Conditional Affordance Learning for Driving in Urban Environments , 2018, CoRL.
[35] Daan Wierstra,et al. Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.
[36] Geoffrey E. Hinton,et al. Feudal Reinforcement Learning , 1992, NIPS.
[37] Claude Sammut,et al. A Framework for Behavioural Cloning , 1995, Machine Intelligence 15.
[38] Mayank Bansal,et al. ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst , 2018, Robotics: Science and Systems.
[39] He He,et al. Imitation Learning by Coaching , 2012, NIPS.
[40] Trevor Darrell,et al. Monocular Plan View Networks for Autonomous Driving , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[41] Pieter Abbeel,et al. Value Iteration Networks , 2016, NIPS.
[42] Germán Ros,et al. CARLA: An Open Urban Driving Simulator , 2017, CoRL.
[43] Jürgen Schmidhuber,et al. Evolving large-scale neural networks for vision-based reinforcement learning , 2013, GECCO '13.
[44] Pieter Abbeel,et al. An Algorithmic Perspective on Imitation Learning , 2018, Found. Trends Robotics.
[45] Jan Peters,et al. Reinforcement learning in robotics: A survey , 2013, Int. J. Robotics Res..
[46] Mica R. Endsley,et al. Theoretical Underpinnings of Situation Awareness, A Critical Review , 2000 .
[47] Xi Chen,et al. Evolution Strategies as a Scalable Alternative to Reinforcement Learning , 2017, ArXiv.
[48] S. Srihari. Mixture Density Networks , 1994 .
[49] Ali Farhadi,et al. Target-driven visual navigation in indoor scenes using deep reinforcement learning , 2016, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[50] Jürgen Schmidhuber,et al. Recurrent World Models Facilitate Policy Evolution , 2018, NeurIPS.
[51] Pushmeet Kohli,et al. CompILE: Compositional Imitation Learning and Execution , 2018, ICML.
[52] Darwin T. Kuan,et al. Autonomous Robotic Vehicle Road Following , 1988, IEEE Trans. Pattern Anal. Mach. Intell..
[53] Amnon Shashua,et al. On the Sample Complexity of End-to-end Training vs. Semantic Abstraction Training , 2016, ArXiv.