Rowan McAllister
Rowan McAllister
Toyota Research Institute
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Zitiert von
Zitiert von
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
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
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
Improving PILCO with Bayesian neural network dynamics models
Y Gal, R McAllister, CE Rasmussen
Data-Efficient Machine Learning workshop, ICML 4, 2016
Learning invariant representations for reinforcement learning without reconstruction
A Zhang, R McAllister, R Calandra, Y Gal, S Levine
arXiv preprint arXiv:2006.10742, 2020
Deep Imitative Models for Flexible Inference, Planning, and Control
N Rhinehart, R McAllister, S Levine
International Conference on Learning Representations, 2018
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
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
Data-efficient reinforcement learning in continuous state-action Gaussian-POMDPs
R McAllister, CE Rasmussen
Advances in Neural Information Processing Systems, 2040-2049, 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
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
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
Hierarchical planning for self-reconfiguring robots using module kinematics
R Fitch, R McAllister
Distributed Autonomous Robotic Systems, 477-490, 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
Heterogeneous-agent trajectory forecasting incorporating class uncertainty
B Ivanovic, KH Lee, P Tokmakov, B Wulfe, R McAllister, A Gaidon, ...
arXiv preprint arXiv:2104.12446, 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
Contingencies from observations: Tractable contingency planning with learned behavior models
N Rhinehart, J He, C Packer, MA Wright, R McAllister, JE Gonzalez, ...
arXiv preprint arXiv:2104.10558, 2021
Bayesian learning for data-efficient control
R McAllister
Department of Engineering, University of Cambridge, 2017
Control-Aware Prediction Objectives for Autonomous Driving
R McAllister, B Wulfe, J Mercat, L Ellis, S Levine, A Gaidon
arXiv preprint arXiv:2204.13319, 2022
Unsupervised exploration with deep model-based reinforcement learning
K Chua, R McAllister, R Calandra, S Levine
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