Rowan McAllister

According to our database, Rowan McAllister authored at least 32 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

CARFF: Conditional Auto-Encoded Radiance Field for 3D Scene Forecasting.

Jiezhi Yang, Khushi Desai, Charles Packer, Harshil Bhatia, Nicholas Rhinehart, Rowan McAllister, Joseph E. Gonzalez

Proceedings of the Computer Vision - ECCV 2024, 2024

2023

Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research.

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

In-Distribution Barrier Functions: Self-Supervised Policy Filters that Avoid Out-of-Distribution States.

Fernando Castañeda, Haruki Nishimura, Rowan Thomas McAllister, Koushil Sreenath, Adrien Gaidon

Proceedings of the Learning for Dynamics and Control Conference, 2023

Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning?

Gunshi Gupta, Tim G. J. Rudner, Rowan Thomas McAllister, Adrien Gaidon, Yarin Gal

Proceedings of the Conference on Causal Learning and Reasoning, 2023

2022

Sociotechnical Specification for the Broader Impacts of Autonomous Vehicles.

Thomas Krendl Gilbert, Aaron J. Snoswell, Michael Dennis, Rowan McAllister, Cathy Wu

CoRR, 2022

Dynamics-Aware Comparison of Learned Reward Functions.

Blake Wulfe, Ashwin Balakrishna, Logan Ellis, Jean Mercat, Rowan McAllister, Adrien Gaidon

CoRR, 2022

Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty.

Boris Ivanovic, Kuan-Hui Lee, Pavel Tokmakov, Blake Wulfe, Rowan McAllister, Adrien Gaidon, Marco Pavone

Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

Control-Aware Prediction Objectives for Autonomous Driving.

Rowan McAllister, Blake Wulfe, Jean Mercat, Logan Ellis, Sergey Levine, Adrien Gaidon

Proceedings of the 2022 International Conference on Robotics and Automation, 2022

Dynamics-Aware Comparison of Learned Reward Functions.

Blake Wulfe, Logan Michael Ellis, Jean Mercat, Rowan Thomas McAllister, Adrien Gaidon

Proceedings of the Tenth International Conference on Learning Representations, 2022

S2Net: Stochastic Sequential Pointcloud Forecasting.

Xinshuo Weng, Junyu Nan, Kuan-Hui Lee, Rowan McAllister, Adrien Gaidon, Nicholas Rhinehart, Kris M. Kitani

Proceedings of the Computer Vision - ECCV 2022, 2022

Is Anyone There? Learning a Planner Contingent on Perceptual Uncertainty.

Charles Packer, Nicholas Rhinehart, Rowan Thomas McAllister, Matthew A. Wright, Xin Wang, Jeff He, Sergey Levine, Joseph E. Gonzalez

Proceedings of the Conference on Robot Learning, 2022

RAP: Risk-Aware Prediction for Robust Planning.

Haruki Nishimura, Jean Mercat, Blake Wulfe, Rowan Thomas McAllister, Adrien Gaidon

Proceedings of the Conference on Robot Learning, 2022

2021

Model-Based Meta-Reinforcement Learning for Flight With Suspended Payloads.

Suneel Belkhale, Rachel Li, Gregory Kahn, Rowan McAllister, Roberto Calandra, Sergey Levine

IEEE Robotics Autom. Lett., 2021

Outcome-Driven Reinforcement Learning via Variational Inference.

Tim G. J. Rudner, Vitchyr Pong, Rowan McAllister, Yarin Gal, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models.

Nicholas Rhinehart, Jeff He, Charles Packer, Matthew A. Wright, Rowan McAllister, Joseph E. Gonzalez, Sergey Levine

Proceedings of the IEEE International Conference on Robotics and Automation, 2021

Learning Invariant Representations for Reinforcement Learning without Reconstruction.

Amy Zhang, Rowan Thomas McAllister, Roberto Calandra, Yarin Gal, Sergey Levine

Proceedings of the 9th International Conference on Learning Representations, 2021

Autonomous Vehicle Vision 2021: ICCV Workshop Summary.

Rui Fan, Nemanja Djuric, Fisher Yu, Rowan McAllister, Ioannis Pitas

Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, 2021

2020

Safety Augmented Value Estimation From Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks.

Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, Felix Li, Rowan McAllister, Joseph E. Gonzalez, Sergey Levine, Francesco Borrelli, Ken Goldberg

IEEE Robotics Autom. Lett., 2020

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?

Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal

Proceedings of the 37th International Conference on Machine Learning, 2020

Deep Imitative Models for Flexible Inference, Planning, and Control.

Nicholas Rhinehart, Rowan McAllister, Sergey Levine

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Extending Deep Model Predictive Control with Safety Augmented Value Estimation from Demonstrations.

Brijen Thananjeyan, Ashwin Balakrishna, Ugo Rosolia, Felix Li, Rowan McAllister, Joseph E. Gonzalez, Sergey Levine, Francesco Borrelli, Ken Goldberg

CoRR, 2019

Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty.

Rowan McAllister, Gregory Kahn, Jeff Clune, Sergey Levine

Proceedings of the International Conference on Robotics and Automation, 2019

PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings.

Nicholas Rhinehart, Rowan McAllister, Kris Kitani, Sergey Levine

Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models.

Kurtland Chua, Roberto Calandra, Rowan McAllister, Sergey Levine

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2017

Bayesian learning for data-efficient control.

Rowan McAllister

PhD thesis, 2017

Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs.

Rowan McAllister, Carl Edward Rasmussen

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning.

Rowan McAllister, Yarin Gal, Alex Kendall, Mark van der Wilk, Amar Shah, Roberto Cipolla, Adrian Weller

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017

2016

Data-Efficient Reinforcement Learning in Continuous-State POMDPs.

Rowan McAllister, Carl Edward Rasmussen

CoRR, 2016

2014

Learned Stochastic Mobility Prediction for Planning with Control Uncertainty on Unstructured Terrain.

Thierry Peynot, Sin-Ting Lui, Rowan McAllister, Robert Fitch, Salah Sukkarieh

J. Field Robotics, 2014

2012

Motion planning and stochastic control with experimental validation on a planetary rover.

Rowan McAllister, Thierry Peynot, Robert Fitch, Salah Sukkarieh

Proceedings of the 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012

Resilient Navigation through Probabilistic Modality Reconfiguration.

Thierry Peynot, Robert Fitch, Rowan McAllister, Alen Alempijevic

Proceedings of the Intelligent Autonomous Systems 12, 2012

2010

Hierarchical Planning for Self-reconfiguring Robots Using Module Kinematics.

Robert Fitch, Rowan McAllister

Proceedings of the Distributed Autonomous Robotic Systems, 2010