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 CalandraFacebook AI ResearchVerified email at fb.com
Kurtland ChuaPrinceton UniversityVerified email at princeton.edu
Robert FitchSchool of Mechanical and Mechatronic Engineering, University of Technology SydneyVerified email at uts.edu.au
Thierry PeynotQueensland University of Technology (QUT)Verified email at qut.edu.au
Mark van der WilkImperial College LondonVerified email at imperial.ac.uk
Amar ShahPhD student, University of CambridgeVerified email at cam.ac.uk
Adrian WellerPrincipal Research Fellow, Machine Learning, University of CambridgeVerified email at eng.cam.ac.uk
Kris KitaniCarnegie Mellon University, Robotics InstituteVerified email at cs.cmu.edu
Salah SukkariehProfessor of Robotics and Intelligent SystemsVerified email at sydney.edu.au
Roberto CipollaProfessor of Information Engineering, University of CambridgeVerified email at eng.cam.ac.uk
Alex KendallUniversity of CambridgeVerified email at cam.ac.uk
Ken GoldbergProfessor, UC Berkeley and UCSFVerified email at berkeley.edu
Ugo RosoliaPostDoc at CaltechVerified email at berkeley.edu
Brijen ThananjeyanPhD Student, University of California, BerkeleyVerified email at berkeley.edu
Francesco BorrelliProfessor of Controls, UC Berkeley, CAVerified email at berkeley.edu
Ashwin BalakrishnaPhD Student in EECS, UC BerkeleyVerified email at berkeley.edu
Angelos FilosUniversity of OxfordVerified email at cs.ox.ac.uk

Rowan McAllister

Toyota Research Institute

Verified email at tri.global - 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	426	2018
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	167*	2017
Improving PILCO with Bayesian neural network dynamics models Y Gal, R McAllister, CE Rasmussen Data-Efficient Machine Learning workshop, ICML 4, 2016	165	2016
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	117	2019
Deep Imitative Models for Flexible Inference, Planning, and Control N Rhinehart, R McAllister, S Levine International Conference on Learning Representations, 2018	56	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	33	2020
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	33	2014
Data-efficient reinforcement learning in continuous state-action Gaussian-POMDPs R McAllister, CE Rasmussen Advances in Neural Information Processing Systems, 2040-2049, 2017	31*	2017
Learning invariant representations for reinforcement learning without reconstruction A Zhang, R McAllister, R Calandra, Y Gal, S Levine arXiv preprint arXiv:2006.10742, 2020	27	2020
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	21*	2020
Hierarchical planning for self-reconfiguring robots using module kinematics R Fitch, R McAllister Distributed Autonomous Robotic Systems, 477-490, 2013	18	2013
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	13	2012
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	12	2019
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	5	2020
Bayesian learning for data-efficient control R McAllister Department of Engineering, University of Cambridge, 2017	5	2017
Unsupervised exploration with deep model-based reinforcement learning K Chua, R McAllister, R Calandra, S Levine	3	2018
Autonomous reconfiguration of a multi-modal mobile robot T Peynot, R Fitch, R McAllister, A Alempijevic Workshop on Automated Diagnosis, Repair and Re-Configuration of Robot …, 2011	3	2011
Bayesian Reinforcement Learning R Mcallister, K Dziugaite Bayesian Reinforcement Learning 35 (March), 2013	2	2013
Resilient navigation through probabilistic modality reconfiguration T Peynot, R Fitch, R McAllister, A Alempijevic Intelligent Autonomous Systems 12, 75-88, 2013	2	2013
Autonomous reconfiguration planning in modular robots R McAllister surfaces 28, 438, 2009	2*	2009

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