| 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 | 692 | 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 | 226* | 2017 |
| Improving PILCO with Bayesian neural network dynamics models Y Gal, R McAllister, CE Rasmussen Data-Efficient Machine Learning workshop, ICML 4, 2016 | 206 | 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 | 191 | 2019 |
| Learning invariant representations for reinforcement learning without reconstruction A Zhang, R McAllister, R Calandra, Y Gal, S Levine arXiv preprint arXiv:2006.10742, 2020 | 121 | 2020 |
| Deep Imitative Models for Flexible Inference, Planning, and Control N Rhinehart, R McAllister, S Levine International Conference on Learning Representations, 2018 | 91 | 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 | 66 | 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 | 66 | 2020 |
| Data-efficient reinforcement learning in continuous state-action Gaussian-POMDPs R McAllister, CE Rasmussen Advances in Neural Information Processing Systems, 2040-2049, 2017 | 39* | 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 | 36 | 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 | 24 | 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 | 22 | 2019 |
| Hierarchical planning for self-reconfiguring robots using module kinematics R Fitch, R McAllister Distributed Autonomous Robotic Systems, 477-490, 2013 | 19 | 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 | 18 | 2012 |
| Outcome-driven reinforcement learning via variational inference TGJ Rudner, V Pong, R McAllister, Y Gal, S Levine Advances in Neural Information Processing Systems 34, 2021 | 5 | 2021 |
| 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 | 5 | 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 | 5 | 2021 |
| 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 | 4 | 2018 |
| Resilient navigation through probabilistic modality reconfiguration T Peynot, R Fitch, R McAllister, A Alempijevic Intelligent Autonomous Systems 12, 75-88, 2013 | 3 | 2013 |
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
