Rowan McAllister

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Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
Yarin GalAssociate Professor, University of OxfordVerified email at cs.ox.ac.uk
Nicholas RhinehartUC BerkeleyVerified email at berkeley.edu
Roberto CalandraProfessor, TU Dresden, Centre for Tactile Internet with Human-in-the-Loop (CeTI)Verified email at tu-dresden.de
Adrien GaidonHead of Machine Learning Research, Toyota Research Institute (TRI)Verified email at tri.global
Robert FitchSchool of Mechanical and Mechatronic Engineering, University of Technology SydneyVerified email at uts.edu.au
Kris KitaniCarnegie Mellon University, Robotics InstituteVerified email at cs.cmu.edu
Mark van der WilkAssociate Professor, University of OxfordVerified email at cs.ox.ac.uk
Kurtland ChuaPrinceton UniversityVerified email at princeton.edu
Thierry PeynotQueensland University of Technology (QUT)Verified email at qut.edu.au
Amar ShahPhD student, University of CambridgeVerified email at cam.ac.uk
Adrian WellerDirector of Research, Machine Learning, University of CambridgeVerified email at eng.cam.ac.uk
Roberto CipollaProfessor of Information Engineering, University of CambridgeVerified email at eng.cam.ac.uk
Alex KendallUniversity of CambridgeVerified email at cam.ac.uk
Joseph E. GonzalezProfessor of Computer Science, UC BerkeleyVerified email at berkeley.edu
Blake WulfeMachine Learning Researcher, Toyota Research Institute (TRI)Verified email at tri.global
Ashwin BalakrishnaResearch Scientist, Toyota Research InstituteVerified email at tri.global
Amy ZhangAssistant Professor of Electrical and Computer Engineering at University of Texas at AustinVerified email at austin.utexas.edu
Angelos FilosDeepMind, University of OxfordVerified email at deepmind.com
Panagiotis TigasIsomoprhic Labs, Oxford UniversityVerified email at robots.ox.ac.uk

Rowan McAllister

Waymo

Verified email at waymo.com - Homepage

Reinforcement Learning Robotics Machine Learning Autonomous Vehicles


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Deep reinforcement learning in a handful of trials using probabilistic dynamics models K Chua, R Calandra, R McAllister, S Levine Advances in Neural Information Processing Systems, 4754-4765, 2018	1173	2018
Learning invariant representations for reinforcement learning without reconstruction A Zhang, R McAllister, R Calandra, Y Gal, S Levine arXiv preprint arXiv:2006.10742, 2020	357	2020
PRECOG: Prediction conditioned on goals in visual multi-agent settings N Rhinehart, R McAllister, K Kitani, S Levine International Conference on Computer Vision, 2821-2830, 2019	355	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, ... International Joint Conferences on Artificial Intelligence, Inc., 2017	346*	2017
Improving PILCO with Bayesian neural network dynamics models Y Gal, R McAllister, CE Rasmussen Data-Efficient Machine Learning workshop, ICML 4, 2016	279	2016
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? A Filos, P Tigas, R McAllister, N Rhinehart, S Levine, Y Gal International Conference on Machine Learning, 2020	151	2020
Deep Imitative Models for Flexible Inference, Planning, and Control N Rhinehart, R McAllister, S Levine International Conference on Learning Representations, 2018	138	2018
Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks B Thananjeyan, A Balakrishna, U Rosolia, F Li, R McAllister, JE Gonzalez, ... IEEE Robotics and Automation Letters 5 (2), 3612-3619, 2020	104	2020
Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads S Belkhale, R Li, G Kahn, R McAllister, R Calandra, S Levine arXiv preprint arXiv:2004.11345, 2020	72	2020
Data-efficient reinforcement learning in continuous state-action Gaussian-POMDPs R McAllister, CE Rasmussen Advances in Neural Information Processing Systems, 2040-2049, 2017	52*	2017
Learned stochastic mobility prediction for planning with control uncertainty on unstructured terrain T Peynot, ST Lui, R McAllister, R Fitch, S Sukkarieh Journal of Field Robotics 31 (6), 969-995, 2014	42	2014
Robustness to out-of-distribution inputs via task-aware generative uncertainty R McAllister, G Kahn, J Clune, S Levine International Conference on Robotics and Automation, 2019	39	2019
Hierarchical planning for self-reconfiguring robots using module kinematics R Fitch, R McAllister Distributed Autonomous Robotic Systems: The 10th International Symposium …, 2013	27	2013
Heterogeneous-agent trajectory forecasting incorporating class uncertainty B Ivanovic, KH Lee, P Tokmakov, B Wulfe, R Mcllister, A Gaidon, ... 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022	25	2022
Motion planning and stochastic control with experimental validation on a planetary rover R McAllister, T Peynot, R Fitch, S Sukkarieh 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2012	19	2012
Control-aware prediction objectives for autonomous driving R McAllister, B Wulfe, J Mercat, L Ellis, S Levine, A Gaidon 2022 International Conference on Robotics and Automation (ICRA), 01-08, 2022	17	2022
Contingencies from observations: Tractable contingency planning with learned behavior models N Rhinehart, J He, C Packer, MA Wright, R McAllister, JE Gonzalez, ... 2021 IEEE International Conference on Robotics and Automation (ICRA), 13663 …, 2021	15	2021
Outcome-driven reinforcement learning via variational inference TGJ Rudner, V Pong, R McAllister, Y Gal, S Levine Advances in Neural Information Processing Systems 34, 13045-13058, 2021	13	2021
Dynamics-aware comparison of learned reward functions B Wulfe, A Balakrishna, L Ellis, J Mercat, R McAllister, A Gaidon arXiv preprint arXiv:2201.10081, 2022	8	2022
Bayesian learning for data-efficient control R McAllister Department of Engineering, University of Cambridge, 2017	7	2017

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