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We propose a framework for planning in unknown dynamic environments with probabilistic safety guarantees using conformal prediction.
J. Van den Berg, M. Lin, and D. Manocha, “Reciprocal velocity obstacles for real-time multi-agent navigation,” in 2008 IEEE international conference on robotics and automation . Ieee, 2008, pp. 1928–1935
1935
Earlier work this paper cites.
Y. Chen, U. Rosolia, C. Fan, A. Ames, and R. Murray, “Reactive motion planning with probabilisticsafety guarantees,” in Conference on Robot Learning . PMLR, 2021, pp. 1958–1970
1970
Earlier work this paper cites.
E. Rimon and D. E. Koditschek, “Exact robot navigation using artificial potential functions,” IEEE transactions on robotics and automation , vol. 8, no. 5, pp. 501–518, 1992
1992
Earlier work this paper cites.
D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” IEEE Robotics & Automation Magazine , vol. 4, no. 1, pp. 23–33, 1997
1997
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
H. G. Tanner, S. G. Loizou, and K. J. Kyriakopoulos, “Nonholonomic navigation and control of cooperating mobile manipulators,” IEEE Transactions on robotics and automation , vol. 19, no. 1, pp. 53–64, 2003
2003
Earlier work this paper cites.
V. Vovk, A. Gammerman, and G. Shafer, Algorithmic learning in a random world . Springer Science & Business Media, 2005
2005
Earlier work this paper cites.
D. V. Dimarogonas
2006
Earlier work this paper cites.
R. Pepy, A. Lambert, and H. Mounier, “Path planning using a dynamic vehicle model,” in 2006 2nd International Conference on Information & Communication Technologies , vol. 1. IEEE, 2006, pp. 781–786
2006
Earlier work this paper cites.
G. Shafer and V. Vovk, “A tutorial on conformal prediction.” Journal of Machine Learning Research , vol. 9, no. 3, 2008
2008
Earlier work this paper cites.
C. Fulgenzi, C. Tay, A. Spalanzani, and C. Laugier, “Probabilistic navigation in dynamic environment using rapidly-exploring random trees and gaussian processes,” in 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2008, pp. 1056–1062
2008
Earlier work this paper cites.
A. Graves and J. Schmidhuber, “Offline handwriting recognition with multidimensional recurrent neural networks,” Advances in neural information processing systems , vol. 21, 2008
2008
Earlier work this paper cites.
P. Trautman and A. Krause, “Unfreezing the robot: Navigation in dense, interacting crowds,” in 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2010, pp. 797–803
2010
Earlier work this paper cites.
N. E. Du Toit and J. W. Burdick, “Robot motion planning in dynamic, uncertain environments,” IEEE Transactions on Robotics , vol. 28, no. 1, pp. 101–115, 2011
2011
Earlier work this paper cites.
M. Phillips and M. Likhachev, “Sipp: Safe interval path planning for dynamic environments,” in 2011 IEEE International Conference on Robotics and Automation . IEEE, 2011, pp. 5628–5635
2011
Earlier work this paper cites.
M. Kuderer, H. Kretzschmar, C. Sprunk, and W. Burgard, “Feature-based prediction of trajectories for socially compliant navigation,” in Robotics: science and systems , 2012
2012
Earlier work this paper cites.
S. Mitsch, K. Ghorbal, and A. Platzer, “On provably safe obstacle avoidance for autonomous robotic ground vehicles,” in Robotics: Science and Systems IX, Technische Universität Berlin, Berlin, Germany, June 24-June 28, 2013 , 2013
2013
Earlier work this paper cites.
G. S. Aoude, B. D. Luders, J. M. Joseph, N. Roy, and J. P. How, “Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns,” Autonomous Robots , vol. 35, no. 1, pp. 51–76, 2013
2013
Earlier work this paper cites.
P. Trautman, J. Ma, R. M. Murray, and A. Krause, “Robot navigation in dense human crowds: the case for cooperation,” in 2013 IEEE international conference on robotics and automation . IEEE, 2013, pp. 2153–2160
2013
Earlier work this paper cites.
A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in 2013 IEEE international conference on acoustics, speech and signal processing . Ieee, 2013, pp. 6645–6649
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
2015
Earlier work this paper cites.
H. Kretzschmar, M. Spies, C. Sprunk, and W. Burgard, “Socially compliant mobile robot navigation via inverse reinforcement learning,” The International Journal of Robotics Research , vol. 35, no. 11, pp. 1289–1307, 2016
2016
Cited alongside, same era.
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, “Social lstm: Human trajectory prediction in crowded spaces,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 961–971
2016
Cited alongside, same era.
M. Arjovsky, A. Shah, and Y. Bengio, “Unitary evolution recurrent neural networks,” in International Conference on Machine Learning . PMLR, 2016, pp. 1120–1128
2016
Cited alongside, same era.
A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “CARLA: An open urban driving simulator,” in Proceedings of the 1st Annual Conference on Robot Learning , 2017, pp. 1–16
2017
Cited alongside, same era.
2020
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2021
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M. Everett, Y. F. Chen, and J. P. How, “Collision avoidance in pedestrian-rich environments with deep reinforcement learning,” IEEE Access , vol. 9, pp. 10 357–10 377, 2021
2021
Later among the works it cites.
P. Kothari, S. Kreiss, and A. Alahi, “Human trajectory forecasting in crowds: A deep learning perspective,” IEEE Transactions on Intelligent Transportation Systems , 2021
2021
Later among the works it cites.
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Y. F. Chen, M. Everett, M. Liu, and J. P. How, “Socially aware motion planning with deep reinforcement learning,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 1343–1350
2017
Cited alongside, same era.
S. Choi, E. Kim, K. Lee, and S. Oh, “Real-time nonparametric reactive navigation of mobile robots in dynamic environments,” Robotics and Autonomous Systems , vol. 91, pp. 11–24, 2017
2017
Cited alongside, same era.
J. F. Fisac, A. Bajcsy, S. L. Herbert, D. Fridovich-Keil, S. Wang, C. J. Tomlin, and A. D. Dragan, “Probabilistically safe robot planning with confidence-based human predictions,” in 14th Robotics: Science and Systems, RSS 2018 . MIT Press Journals, 2018
2018
Cited alongside, same era.
J. Lei, M. G’Sell, A. Rinaldo, R. J. Tibshirani, and L. Wasserman, “Distribution-free predictive inference for regression,” Journal of the American Statistical Association , vol. 113, no. 523, pp. 1094–1111, 2018
2018
Cited alongside, same era.
A. Rasouli, I. Kotseruba, T. Kunic, and J. K. Tsotsos, “Pie: A large-scale dataset and models for pedestrian intention estimation and trajectory prediction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 6262–6271
2019
Cited alongside, same era.
N. Rhinehart, R. McAllister, K. Kitani, and S. Levine, “Precog: Prediction conditioned on goals in visual multi-agent settings,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 2821–2830
2019
Cited alongside, same era.
L. Bortolussi, F. Cairoli, N. Paoletti, S. A. Smolka, and S. D. Stoller, “Neural predictive monitoring,” in International Conference on Runtime Verification . Springer, 2019, pp. 129–147
2019
Cited alongside, same era.
R. J. Tibshirani, R. Foygel Barber, E. Candes, and A. Ramdas, “Conformal prediction under covariate shift,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
K. Majd, S. Yaghoubi, T. Yamaguchi, B. Hoxha, D. Prokhorov, and G. Fainekos, “Safe navigation in human occupied environments using sampling and control barrier functions,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 5794–5800
2021
Later among the works it cites.
A. Thomas, F. Mastrogiovanni, and M. Baglietto, “Probabilistic collision constraint for motion planning in dynamic environments,” in International Conference on Intelligent Autonomous Systems . Springer, 2021, pp. 141–154
2021
Later among the works it cites.
H. Zhu, F. M. Claramunt, B. Brito, and J. Alonso-Mora, “Learning interaction-aware trajectory predictions for decentralized multi-robot motion planning in dynamic environments,” IEEE Robotics and Automation Letters , vol. 6, no. 2, pp. 2256–2263, 2021
2021
Later among the works it cites.
M. Omainska, J. Yamauchi, T. Beckers, T. Hatanaka, S. Hirche, and M. Fujita, “Gaussian process-based visual pursuit control with unknown target motion learning in three dimensions,” SICE Journal of Control, Measurement, and System Integration , vol. 14, no. 1, pp. 116–127, 2021
2021
Later among the works it cites.
T. Du, S. Ji, L. Shen, Y. Zhang, J. Li, J. Shi, C. Fang, J. Yin, R. Beyah, and T. Wang, “Cert-rnn: Towards certifying the robustness of recurrent neural networks,” in Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security , 2021, pp. 516–534
2021
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2021
Later among the works it cites.
2021
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K. Stankeviciute, A. M Alaa, and M. van der Schaar, “Conformal time-series forecasting,” Advances in Neural Information Processing Systems , vol. 34, pp. 6216–6228, 2021
2021
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2022
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S. X. Wei, A. Dixit, S. Tomar, and J. W. Burdick, “Moving obstacle avoidance: a data-driven risk-aware approach,” IEEE Control Systems Letters , 2022
2022
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A. Wang, C. Mavrogiannis, and A. Steinfeld, “Group-based motion prediction for navigation in crowded environments,” in Conference on Robot Learning . PMLR, 2022, pp. 871–882
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M. Fontana, G. Zeni, and S. Vantini, “Conformal prediction: A unified review of theory and new challenges,” Bernoulli , vol. 29, no. 1, pp. 1 – 23, 2023
2023
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