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Deep Neural Network-based systems are now the state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness.
Neuronlike adaptive elements that can solve difficult learning control problems
A. G. Barto, R. S. Sutton, and C. W. Anderson · 1983
Earlier work this paper cites.
Consideration of risk in reinforcement learning
M. Heger · 1994
Earlier work this paper cites.
Adversarial reinforcement learning
W. Uther and M. Veloso · 1997
Earlier work this paper cites.
Risk-Sensitive Approaches for Reinforcement Learning
P. Geibel · 2006
Earlier work this paper cites.
Reciprocal n-body collision avoidance
J. P. van den Berg, S. J. Guy, M. C. Lin, and D. Manocha · 2009
Earlier work this paper cites.
Unfreezing the robot: Navigation in dense, interacting crowds
P. Trautman and A. Krause · 2010
Earlier work this paper cites.
Practical bayesian optimization of machine learning algorithms
J. Snoek, H. Larochelle, and R. P. Adams · 2012
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
I. Goodfellow, J. Shlens, and C. Szegedy · 2015
Earlier work this paper cites.
A comprehensive survey on safe reinforcement learning
J. García and F. Fernández · 2015
Earlier work this paper cites.
Risk-sensitive and Efficient Reinforcement Learning Algorithms
A. Tamar · 2015
Earlier work this paper cites.
Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis · 2015
Earlier work this paper cites.
Accessorize to a crime: Real and stealthy attacks on state-of-the-art face recognition
M. Sharif, S. Bhagavatula, L. Bauer, and M. K. Reiter · 2016
Earlier work this paper cites.
Distillation as a defense to adversarial perturbations against deep neural networks
N. Papernot, P. McDaniel, X. Wu, S. Jha, and A. Swami · 2016
Earlier work this paper cites.
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
S. Gu, E. Holly, T. Lillicrap, and S. Levine · 2017
Earlier work this paper cites.
Adversarial examples in the physical world
A. Kurakin, I. J. Goodfellow, and S. Bengio · 2017
Earlier work this paper cites.
Adversarial machine learning at scale
A. Kurakin, I. J. Goodfellow, and S. Bengio · 2017
Earlier work this paper cites.
Delving into adversarial attacks on deep policies
J. Kos and D. Song · 2017
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Adversarial examples are not easily detected: Bypassing ten detection methods
N. Carlini and D. Wagner · 2017
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Adversarial example defenses: Ensembles of weak defenses are not strong
W. He, J. Wei, X. Chen, N. Carlini, and D. Song · 2017
Cited alongside, same era.
Reluplex: An efficient SMT solver for verifying deep neural networks
G. Katz, C. W. Barrett, D. L. Dill, K. Julian, and M. J. Kochenderfer · 2017
Cited alongside, same era.
An approach to reachability analysis for feed-forward relu neural networks
A. Lomuscio and L. Maganti · 2017
Cited alongside, same era.
Formal verification of piece-wise linear feed-forward neural networks
Adversarial risk and the dangers of evaluating against weak attacks
J. Uesato, B. O’Donoghue, P. Kohli, and A. van den Oord · 2018
Later among the works it cites.
Certified defenses against adversarial examples
A. Raghunathan, J. Steinhardt, and P. Liang · 2018
Later among the works it cites.
Towards fast computation of certified robustness for relu networks
T. Weng, H. Zhang, H. Chen, Z. Song, C. Hsieh, L. Daniel, D. Boning, and I. Dhillon · 2018
Later among the works it cites.
Fast and effective robustness certification
G. Singh, T. Gehr, M. Mirman, M. Püschel, and M. Vechev · 2018
Later among the works it cites.
Efficient formal safety analysis of neural networks
S. Wang, K. Pei, J. Whitehouse, J. Yang, and S. Jana · 2018
Later among the works it cites.
Efficient neural network robustness certification with general activation functions
H. Zhang, T.-W. Weng, P.-Y. Chen, C.-J. Hsieh, and L. Daniel · 2018
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R. Ehlers · 2017
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Safety verification of deep neural networks
X. Huang, M. Kwiatkowska, S. Wang, and M. Wu · 2017
Cited alongside, same era.
Epopt: Learning robust neural network policies using model ensembles
A. Rajeswaran, S. Ghotra, B. Ravindran, and S. Levine · 2017
Cited alongside, same era.
Robust adversarial reinforcement learning
L. Pinto, J. Davidson, R. Sukthankar, and A. Gupta · 2017
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Uncertainty-aware reinforcement learning for collision avoidance
G. Kahn, A. Villaflor, V. Pong, P. Abbeel, and S. Levine · 2017
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Deeply AggreVaTeD: Differentiable imitation learning for sequential prediction
W. Sun, A. Venkatraman, G. J. Gordon, B. Boots, and J. A. Bagnell · 2017
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Safe model-based reinforcement learning with stability guarantees
F. Berkenkamp, M. Turchetta, A. Schoellig, and A. Krause · 2017
Cited alongside, same era.
Later among the works it cites.
Domain randomization for simulation-based policy optimization with transferability assessment
F. Muratore, F. Treede, M. Gienger, and J. Peters · 2018
Later among the works it cites.
Feature squeezing: Detecting adversarial examples in deep neural networks
W. Xu, D. Evans, and Y. Qi · 2018
Later among the works it cites.
Ai2: Safety and robustness certification of neural networks with abstract interpretation
T. Gehr, M. Mirman, D. Drachsler-Cohen, P. Tsankov, S. Chaudhuri, and M. Vechev · 2018
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Getting robots unfrozen and unlost in dense pedestrian crowds
T. Fan, X. Cheng, J. Pan, P. Long, W. Liu, R. Yang, and D. Manocha · 2019
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Adversarial examples: Attacks and defenses for deep learning
X. Yuan, P. He, Q. Zhu, R. R. Bhat, and X. Li · 2019
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Experimental security research of Tesla Autopilot, 03 2019
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Evaluating robustness of neural networks with mixed integer programming
V. Tjeng, K. Y. Xiao, and R. Tedrake · 2019
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Cnn-cert: An efficient framework for certifying robustness of convolutional neural networks
A. Boopathy, T.-W. Weng, P.-Y. Chen, S. Liu, and L. Daniel · 2019
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Online Robustness Training for Deep Reinforcement Learning
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Safe Reinforcement Learning with Model Uncertainty Estimates
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