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ORCIDRowan McAllister
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2020 – today
- 2024
[i20]Jiezhi Yang, Khushi Desai, Charles Packer, Harshil Bhatia, Nicholas Rhinehart, Rowan McAllister, Joseph Gonzalez:
CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting. CoRR abs/2401.18075 (2024)- 2023
- [c23]Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal:
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CLeaR 2023: 386-407 - [c22]Fernando Castañeda, Haruki Nishimura, Rowan Thomas McAllister, Koushil Sreenath, Adrien Gaidon:
In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States. L4DC 2023: 286-299 - [c21]Cole Gulino, Justin Fu, Wenjie Luo, George Tucker, Eli Bronstein, Yiren Lu, Jean Harb, Xinlei Pan, Yan Wang, Xiangyu Chen, John D. Co-Reyes, Rishabh Agarwal, Rebecca Roelofs, Yao Lu, Nico Montali, Paul Mougin, Zoey Yang, Brandyn White, Aleksandra Faust, Rowan McAllister, Dragomir Anguelov, Benjamin Sapp:
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research. NeurIPS 2023 - [i19]Fernando Castañeda, Haruki Nishimura, Rowan McAllister, Koushil Sreenath, Adrien Gaidon:
In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States. CoRR abs/2301.12012 (2023) - [i18]Cole Gulino, Justin Fu, Wenjie Luo, George Tucker, Eli Bronstein, Yiren Lu, Jean Harb, Xinlei Pan, Yan Wang, Xiangyu Chen, John D. Co-Reyes, Rishabh Agarwal, Rebecca Roelofs, Yao Lu, Nico Montali, Paul Mougin, Zoey Yang, Brandyn White, Aleksandra Faust
, Rowan McAllister, Dragomir Anguelov, Benjamin Sapp:
Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research. CoRR abs/2310.08710 (2023) - [i17]Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal:
Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? CoRR abs/2312.17168 (2023) - 2022
- [c20]Haruki Nishimura, Jean Mercat, Blake Wulfe, Rowan Thomas McAllister, Adrien Gaidon:
RAP: Risk-Aware Prediction for Robust Planning. CoRL 2022: 381-392 - [c19]Charles Packer, Nicholas Rhinehart, Rowan Thomas McAllister, Matthew A. Wright, Xin Wang, Jeff He, Sergey Levine, Joseph E. Gonzalez:
Is Anyone There? Learning a Planner Contingent on Perceptual Uncertainty. CoRL 2022: 1607-1617 - [c18]Xinshuo Weng, Junyu Nan, Kuan-Hui Lee, Rowan McAllister, Adrien Gaidon, Nicholas Rhinehart, Kris M. Kitani:
S2Net: Stochastic Sequential Pointcloud Forecasting. ECCV (27) 2022: 549-564 - [c17]Blake Wulfe, Logan Michael Ellis, Jean Mercat, Rowan Thomas McAllister, Adrien Gaidon:
Dynamics-Aware Comparison of Learned Reward Functions. ICLR 2022 - [c16]Rowan McAllister, Blake Wulfe, Jean Mercat, Logan Ellis, Sergey Levine, Adrien Gaidon:
Control-Aware Prediction Objectives for Autonomous Driving. ICRA 2022: 1-8 - [c15]Boris Ivanovic, Kuan-Hui Lee, Pavel Tokmakov, Blake Wulfe, Rowan McAllister, Adrien Gaidon, Marco Pavone:
Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty. IROS 2022: 12196-12203 - [i16]Blake Wulfe, Ashwin Balakrishna, Logan Ellis, Jean Mercat, Rowan McAllister, Adrien Gaidon:
Dynamics-Aware Comparison of Learned Reward Functions. CoRR abs/2201.10081 (2022) - [i15]Rowan McAllister, Blake Wulfe, Jean Mercat, Logan Ellis, Sergey Levine, Adrien Gaidon:
Control-Aware Prediction Objectives for Autonomous Driving. CoRR abs/2204.13319 (2022) - [i14]Thomas Krendl Gilbert, Aaron J. Snoswell, Michael Dennis, Rowan McAllister, Cathy Wu:
Sociotechnical Specification for the Broader Impacts of Autonomous Vehicles. CoRR abs/2205.07395 (2022) - [i13]Haruki Nishimura, Jean Mercat, Blake Wulfe, Rowan McAllister, Adrien Gaidon:
RAP: Risk-Aware Prediction for Robust Planning. CoRR abs/2210.01368 (2022) - 2021
- [j3]Suneel Belkhale, Rachel Li, Gregory Kahn, Rowan McAllister, Roberto Calandra, Sergey Levine:
Model-Based Meta-Reinforcement Learning for Flight With Suspended Payloads. IEEE Robotics Autom. Lett. 6(2): 1471-1478 (2021) - [c14]Rui Fan, Nemanja Djuric, Fisher Yu, Rowan McAllister, Ioannis Pitas:
Autonomous Vehicle Vision 2021: ICCV Workshop Summary. ICCVW 2021: 3088-3095 - [c13]Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, Sergey Levine:
Learning Invariant Representations for Reinforcement Learning without Reconstruction. ICLR 2021 - [c12]Nicholas Rhinehart, Jeff He, Charles Packer, Matthew A. Wright, Rowan McAllister, Joseph E. Gonzalez, Sergey Levine:
Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models. ICRA 2021: 13663-13669 - [c11]Tim G. J. Rudner, Vitchyr Pong, Rowan McAllister, Yarin Gal, Sergey Levine:
Outcome-Driven Reinforcement Learning via Variational Inference. NeurIPS 2021: 13045-13058 - [i12]Tim G. J. Rudner, Vitchyr H. Pong, Rowan McAllister, Yarin Gal, Sergey Levine:
Outcome-Driven Reinforcement Learning via Variational Inference. CoRR abs/2104.10190 (2021) - [i11]Nicholas Rhinehart, Jeff He, Charles Packer, Matthew A. Wright, Rowan McAllister, Joseph E. Gonzalez, Sergey Levine:
Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models. CoRR abs/2104.10558 (2021) - [i10]Boris Ivanovic, Kuan-Hui Lee, Pavel Tokmakov, Blake Wulfe, Rowan McAllister, Adrien Gaidon, Marco Pavone:
Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty. CoRR abs/2104.12446 (2021) - 2020
- [j2]Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, Felix Li, Rowan McAllister, Joseph E. Gonzalez, Sergey Levine, Francesco Borrelli, Ken Goldberg:
Safety Augmented Value Estimation From Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks. IEEE Robotics Autom. Lett. 5(2): 3612-3619 (2020) - [c10]Nicholas Rhinehart, Rowan McAllister, Sergey Levine:
Deep Imitative Models for Flexible Inference, Planning, and Control. ICLR 2020 - [c9]Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal:
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? ICML 2020: 3145-3153 - [i9]Suneel Belkhale, Rachel Li, Gregory Kahn, Rowan McAllister, Roberto Calandra, Sergey Levine:
Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads. CoRR abs/2004.11345 (2020) - [i8]Amy Zhang, Rowan McAllister, Roberto Calandra, Yarin Gal, Sergey Levine:
Learning Invariant Representations for Reinforcement Learning without Reconstruction. CoRR abs/2006.10742 (2020) - [i7]Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal:
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? CoRR abs/2006.14911 (2020)
2010 – 2019
- 2019
- [c8]Nicholas Rhinehart, Rowan McAllister, Kris Kitani, Sergey Levine:
PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings. ICCV 2019: 2821-2830 - [c7]Rowan McAllister, Gregory Kahn, Jeff Clune, Sergey Levine:
Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty. ICRA 2019: 2083-2089 - [i6]Nicholas Rhinehart, Rowan McAllister, Kris M. Kitani, Sergey Levine:
PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings. CoRR abs/1905.01296 (2019) - [i5]Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, Felix Li, Rowan McAllister, Joseph E. Gonzalez, Sergey Levine, Francesco Borrelli, Ken Goldberg:
Extending Deep Model Predictive Control with Safety Augmented Value Estimation from Demonstrations. CoRR abs/1905.13402 (2019) - 2018
- [c6]Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine:
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. NeurIPS 2018: 4759-4770 - [i4]Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine:
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. CoRR abs/1805.12114 (2018) - [i3]Nicholas Rhinehart, Rowan McAllister, Sergey Levine:
Deep Imitative Models for Flexible Inference, Planning, and Control. CoRR abs/1810.06544 (2018) - [i2]Rowan McAllister, Gregory Kahn, Jeff Clune, Sergey Levine:
Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty. CoRR abs/1812.10687 (2018) - 2017
- [b1]Rowan McAllister:
Bayesian learning for data-efficient control. University of Cambridge, UK, 2017 - [c5]Rowan McAllister, Yarin Gal, Alex Kendall, Mark van der Wilk, Amar Shah, Roberto Cipolla, Adrian Weller:
Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning. IJCAI 2017: 4745-4753 - [c4]Rowan McAllister, Carl Edward Rasmussen:
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs. NIPS 2017: 2040-2049 - 2016
- [i1]Rowan McAllister, Carl Edward Rasmussen:
Data-Efficient Reinforcement Learning in Continuous-State POMDPs. CoRR abs/1602.02523 (2016) - 2014
- [j1]Thierry Peynot, Sin-Ting Lui, Rowan McAllister, Robert Fitch, Salah Sukkarieh:
Learned Stochastic Mobility Prediction for Planning with Control Uncertainty on Unstructured Terrain. J. Field Robotics 31(6): 969-995 (2014) - 2012
- [c3]Thierry Peynot, Robert Fitch, Rowan McAllister, Alen Alempijevic:
Resilient Navigation through Probabilistic Modality Reconfiguration. IAS (2) 2012: 75-88 - [c2]Rowan McAllister, Thierry Peynot, Robert Fitch, Salah Sukkarieh:
Motion planning and stochastic control with experimental validation on a planetary rover. IROS 2012: 4716-4723 - 2010
- [c1]Robert Fitch, Rowan McAllister:
Hierarchical Planning for Self-reconfiguring Robots Using Module Kinematics. DARS 2010: 477-490
Coauthor Index
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last updated on 2024-11-22 20:39 CET by the dblp team
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