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Deep Imitative Models for Flexible Inference, Planning, and Control.

N. Rhinehart, R. McAllister, and S. Levine. ICLR, OpenReview.net, (2020)

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James McAllister

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Extending Deep Model Predictive Control with Safety Augmented Value Estimation from DemonstrationsB. Thananjeyan, A. Balakrishna, U. Rosolia, F. Li, R. McAllister, J. Gonzalez, S. Levine, F. Borrelli, and K. Goldberg. (2019)cite arxiv:1905.13402.Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty.R. McAllister, G. Kahn, J. Clune, and S. Levine. ICRA, page 2083-2089. IEEE, (2019)Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty.B. Ivanovic, K. Lee, P. Tokmakov, B. Wulfe, R. McAllister, A. Gaidon, and M. Pavone. IROS, page 12196-12203. IEEE, (2022)Bayesian learning for data-efficient control.R. McAllister. University of Cambridge, UK, (2017)British Library, EThOS.S2Net: Stochastic Sequential Pointcloud Forecasting.X. Weng, J. Nan, K. Lee, R. McAllister, A. Gaidon, N. Rhinehart, and K. Kitani. ECCV (27), volume 13687 of Lecture Notes in Computer Science, page 549-564. Springer, (2022)Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research.C. Gulino, J. Fu, W. Luo, G. Tucker, E. Bronstein, Y. Lu, J. Harb, X. Pan, Y. Wang, X. Chen and 12 other author(s). CoRR, (2023)Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models.N. Rhinehart, J. He, C. Packer, M. Wright, R. McAllister, J. Gonzalez, and S. Levine. ICRA, page 13663-13669. IEEE, (2021)PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings.N. Rhinehart, R. McAllister, K. Kitani, and S. Levine. ICCV, page 2821-2830. IEEE, (2019)Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning.R. McAllister, Y. Gal, A. Kendall, M. van der Wilk, A. Shah, R. Cipolla, and A. Weller. IJCAI, page 4745-4753. ijcai.org, (2017)Deep Imitative Models for Flexible Inference, Planning, and Control.N. Rhinehart, R. McAllister, and S. Levine. CoRR, (2018)

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Deep Imitative Models for Flexible Inference, Planning, and Control.

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James McAllister

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Deep Imitative Models for Flexible Inference, Planning, and Control.

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James McAllister

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Deep Imitative Models for Flexible Inference, Planning, and Control.