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This paper presents an approach for learning motion planners that are accompanied with probabilistic guarantees of success on new environments that hold uniformly for any disturbance to the robot's dynamics within an admissible set.
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Tim Wheeler, “Optimal Trajectories & Control for Automobiles,” https://web.stanford.edu/class/aa222/cgi-bin/wordpress/wp-content/uploads/2019/04/wheeler.pdf , 2014
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K. Karydis, I. Poulakakis, J. Sun, and H. G. Tanner, “Probabilistically valid stochastic extensions of deterministic models for systems with uncertainty,” The International Journal of Robotics Research , vol. 34, no. 10, pp. 1278–1295, 2015
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D. Aksaray, A. Jones, Z. Kong, M. Schwager, and C. Belta, “Q-learning for robust satisfaction of signal temporal logic specifications,” in Proceedings of the IEEE Conference on Decision and Control , 2016, pp. 6565–6570
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A. Majumdar and R. Tedrake, “Funnel libraries for real-time robust feedback motion planning,” The International Journal of Robotics Research , vol. 36, no. 8, pp. 947–982, 2017
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J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems , 2017, pp. 23–30
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I. Higgins, A. Pal, A. Rusu, L. Matthey, C. Burgess, A. Pritzel, M. Botvinick, C. Blundell, and A. Lerchner, “DARLA: Improving zero-shot transfer in reinforcement learning,” in Proceedings of the International Conference on Machine Learning , 2017, pp. 1480–1490
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M. K. Helwa, A. Heins, and A. P. Schoellig, “Provably robust learning-based approach for high-accuracy tracking control of lagrangian systems,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 1587–1594, 2019
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Cited alongside, same era.
I. R. Manchester and J.-J. E. Slotine, “Control contraction metrics: Convex and intrinsic criteria for nonlinear feedback design,” IEEE Transactions on Automatic Control , vol. 62, no. 6, pp. 3046–3053, 2017
2017
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S. Singh, A. Majumdar, J.-J. Slotine, and M. Pavone, “Robust online motion planning via contraction theory and convex optimization,” in Proceedings of the IEEE International Conference on Robotics and Automation , 2017, pp. 5883–5890
2017
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2017
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N. Sünderhauf, O. Brock, W. Scheirer, R. Hadsell, D. Fox, J. Leitner, B. Upcroft, P. Abbeel, W. Burgard, M. Milford, et al. , “The limits and potentials of deep learning for robotics,” The International Journal of Robotics Research , vol. 37, no. 4-5, pp. 405–420, 2018
2018
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2018
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R. Houthooft, R. Y. Chen, P. Isola, B. C. Stadie, F. Wolski, J. Ho, and P. Abbeel, “Evolved policy gradients,” in Proceedings of the International Conference on Neural Information Processing Systems , 2018, pp. 5405–5414
2018
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2018
Cited alongside, same era.
L. Janson, E. Schmerling, and M. Pavone, “Monte carlo motion planning for robot trajectory optimization under uncertainty,” in Robotics Research . Springer, 2018, pp. 343–361
2018
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S. Bogomolov, M. Forets, G. Frehse, K. Potomkin, and C. Schilling, “Juliareach: a toolbox for set-based reachability,” in Proceedings of the 22nd ACM International Conference on Hybrid Systems: Computation and Control , 2019, pp. 39–44
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S. Veer and A. Majumdar, “Probably approximately correct vision-based planning using motion primitives,” in Proceedings of the Conference on Robot Learning , 2020
2020
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2020
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A. Zhang, R. T. McAllister, R. Calandra, Y. Gal, and S. Levine, “Learning invariant representations for reinforcement learning without reconstruction,” in Proceedings of the International Conference Learning Representations , 2020
2020
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R. Agarwal, M. C. Machado, P. S. Castro, and M. G. Bellemare, “Contrastive behavioral similarity embeddings for generalization in reinforcement learning,” in International Conference on Learning Representations , 2020
2020
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2020
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A. Z. Ren, S. Veer, and A. Majumdar, “Generalization Guarantees for Imitation Learning,” in Proceedings of the Conference on Robot Learning , 2020
2020
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K. Leung, N. Arechiga, and M. Pavone, “Back-propagation through signal temporal logic specifications: Infusing logical structure into gradient-based methods,” in Proceedings of the Workshop on the Algorithmic Foundations of Robotics , 2020, pp. 432–449
2020
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A. Majumdar, A. Farid, and A. Sonar, “PAC-Bayes control: learning policies that provably generalize to novel environments,” The International Journal of Robotics Research , vol. 40, no. 2-3, pp. 574–593, 2021
2021
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2021
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A. Sonar, V. Pacelli, and A. Majumdar, “Invariant policy optimization: Towards stronger generalization in reinforcement learning,” in Proceedings of the Learning for Dynamics and Control Conference , 2021, pp. 21–33
2021
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2021
Closest in time.