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Control barrier functions are widely used to enforce safety properties in robot motion planning and control.
O. Khatib, “Real-Time Obstacle Avoidance for Manipulators and Mobile Robots,” The International Journal of Robotics Research
1986
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1987
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1992
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S. Prajna, “Barrier certificates for nonlinear model validation,” in Conference on Decision and Control (CDC)
2003
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F. Alizadeh and D. Goldfarb, “Second-order cone programming,” Mathematical programming
2003
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S. Prajna and A. Jadbabaie, “Safety verification of hybrid systems using barrier certificates,” in Hybrid Systems: Computation and Control
2004
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P. Wieland and F. Allgöwer, “Constructive safety using control barrier functions,” in IFAC Proceedings Volumes
2007
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A. Hornung, K. Wurm, M. Bennewitz, C. Stachniss, and W. Burgard, “Octomap: An efficient probabilistic 3D mapping framework based on octrees,” Autonomous Robots
2013
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A. D. Ames, K. Galloway, K. Sreenath, and J. W. Grizzle, “Rapidly exponentially stabilizing control Lyapunov functions and hybrid zero dynamics,” IEEE Transactions on Automatic Control
2014
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D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint: 1412.6980
2015
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T. Schaul, J. Quan, I. Antonoglou, and D. Silver, “Prioritized experience replay,” in Int. Conf. on Learning Representations (ICLR)
2016
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A. Ames, X. Xu, J. Grizzle, and P. Tabuada, “Control barrier function based quadratic programs for safety critical systems,” IEEE Transactions on Automatic Control
2016
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E. Coumans and Y. Bai, “PyBullet, a Python module for physics simulation for games, robotics and machine learning.” http://pybullet.org , 2016
2016
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H. Oleynikova, Z. Taylor, M. Fehr, R. Siegwart, and J. Nieto, “Voxblox: Incremental 3d euclidean signed distance fields for on-board mav planning,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
D. Rolnick, A. Ahuja, J. Schwarz, T. Lillicrap, and G. Wayne, “Experience replay for continual learning,” in AAAI Conference on Artificial Intelligence
2018
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A. Ames, S. Coogan, M. Egerstedt, G. Notomista, K. Sreenath, and P. Tabuada, “Control barrier functions: Theory and applications,” in European Control Conference (ECC)
2019
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J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, “DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2019
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L. Han, F. Gao, B. Zhou, and S. Shen, “Fiesta: Fast incremental euclidean distance fields for online motion planning of aerial robots,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
2019
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2017
Cited alongside, same era.
P. Glotfelter, J. Cortés, and M. Egerstedt, “Nonsmooth barrier functions with applications to multi-robot systems,” IEEE Control Systems Letters
2017
Cited alongside, same era.
X. Xu, T. Waters, D. Pickem, P. Glotfelter, M. Egerstedt, P. Tabuada, J. W. Grizzle, and A. D. Ames, “Realizing simultaneous lane keeping and adaptive speed regulation on accessible mobile robot testbeds,” in IEEE Conference on Control Technology and Applications (CCTA)
2017
Cited alongside, same era.
D. Yarotsky, “Error bounds for approximations with deep relu networks,” Neural Networks
2017
Cited alongside, same era.
J. Cortés and M. Egerstedt, “Coordinated control of multi-robot systems: A survey,” SICE Journal of Control, Measurement, and System Integration
2017
Cited alongside, same era.
L. M. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger, “Occupancy networks: Learning 3d reconstruction in function space,” IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2019
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B. Bailey, Z. Ji, M. Telgarsky, and R. Xian, “Approximation power of random neural networks,” ArXiv
2019
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2020
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A. Gropp, L. Yariv, N. Haim, M. Atzmon, and Y. Lipman, “Implicit geometric regularization for learning shapes,” in International Conference on Machine Learning
2020
Closest in time.
M. Srinivasan, A. Dabholkar, S. Coogan, and P. Vela, “Synthesis of control barrier functions using a supervised machine learning approach,” arXiv preprint:2003.04950
2020
Closest in time.