Hierarchical Planning for Self-reconfiguring Robots Using Module Kinematics. - Robert Fitch; Rowan McAllister (DARS, 2010)

Resilient Navigation through Probabilistic Modality Reconfiguration. - Thierry Peynot; Robert Fitch; Rowan McAllister; Alen Alempijevic (IAS (2), 2012)

Motion planning and stochastic control with experimental validation on a planetary rover. - Rowan McAllister; Thierry Peynot; Robert Fitch; Salah Sukkarieh (IROS, 2012)

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)

Data-Efficient Reinforcement Learning in Continuous-State POMDPs. - Rowan McAllister; Carl Edward Rasmussen (CoRR, 2016)

Concrete Problems for Autonomous Vehicle Safety: Advantages of Bayesian Deep Learning. - Rowan McAllister; Yarin Gal; Alex Kendall; Mark van der Wilk; Amar Shah 001; Roberto Cipolla; Adrian Weller (IJCAI, 2017)

Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs. - Rowan McAllister; Carl Edward Rasmussen (NIPS, 2017)

Bayesian learning for data-efficient control. - Rowan McAllister (2017)

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models. - Kurtland Chua; Roberto Calandra; Rowan McAllister; Sergey Levine (NeurIPS, 2018)

PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings. - Nicholas Rhinehart; Rowan McAllister; Kris Kitani; Sergey Levine (ICCV, 2019)

Robustness to Out-of-Distribution Inputs via Task-Aware Generative Uncertainty. - Rowan McAllister; Gregory Kahn; Jeff Clune; Sergey Levine (ICRA, 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)

Deep Imitative Models for Flexible Inference, Planning, and Control. - Nicholas Rhinehart; Rowan McAllister; Sergey Levine (ICLR, 2020)

Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? - Angelos Filos; Panagiotis Tigas; Rowan McAllister; Nicholas Rhinehart; Sergey Levine; Yarin Gal (ICML, 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)

Autonomous Vehicle Vision 2021: ICCV Workshop Summary. - Rui Fan 001; Nemanja Djuric; Fisher Yu 001; Rowan McAllister; Ioannis Pitas (ICCVW, 2021)

Learning Invariant Representations for Reinforcement Learning without Reconstruction. - Amy Zhang 001; Rowan Thomas McAllister; Roberto Calandra; Yarin Gal; Sergey Levine (ICLR, 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 (ICRA, 2021)

Outcome-Driven Reinforcement Learning via Variational Inference. - Tim G. J. Rudner; Vitchyr Pong; Rowan McAllister; Yarin Gal; Sergey Levine (NeurIPS, 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)

RAP: Risk-Aware Prediction for Robust Planning. - Haruki Nishimura; Jean Mercat; Blake Wulfe; Rowan Thomas McAllister; Adrien Gaidon (CoRL, 2022)

Is Anyone There? Learning a Planner Contingent on Perceptual Uncertainty. - Charles Packer; Nicholas Rhinehart; Rowan Thomas McAllister; Matthew A. Wright; Xin Wang 066; Jeff He; Sergey Levine; Joseph E. Gonzalez (CoRL, 2022)

S2Net: Stochastic Sequential Pointcloud Forecasting. - Xinshuo Weng; Junyu Nan; Kuan-Hui Lee; Rowan McAllister; Adrien Gaidon; Nicholas Rhinehart; Kris M. Kitani (ECCV (27), 2022)

Dynamics-Aware Comparison of Learned Reward Functions. - Blake Wulfe; Logan Michael Ellis; Jean Mercat; Rowan Thomas McAllister; Adrien Gaidon (ICLR, 2022)

Control-Aware Prediction Objectives for Autonomous Driving. - Rowan McAllister; Blake Wulfe; Jean Mercat; Logan Ellis; Sergey Levine; Adrien Gaidon (ICRA, 2022)

Heterogeneous-Agent Trajectory Forecasting Incorporating Class Uncertainty. - Boris Ivanovic; Kuan-Hui Lee; Pavel Tokmakov; Blake Wulfe; Rowan McAllister; Adrien Gaidon; Marco Pavone 001 (IROS, 2022)

Dynamics-Aware Comparison of Learned Reward Functions. - Blake Wulfe; Ashwin Balakrishna; Logan Ellis; Jean Mercat; Rowan McAllister; Adrien Gaidon (CoRR, 2022)

Sociotechnical Specification for the Broader Impacts of Autonomous Vehicles. - Thomas Krendl Gilbert; Aaron J. Snoswell; Michael Dennis 001; Rowan McAllister; Cathy Wu 002 (CoRR, 2022)

Can Active Sampling Reduce Causal Confusion in Offline Reinforcement Learning? - Gunshi Gupta; Tim G. J. Rudner; Rowan Thomas McAllister; Adrien Gaidon; Yarin Gal (CLeaR, 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 (L4DC, 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 001; Jean Harb; Xinlei Pan; Yan Wang 051; Xiangyu Chen 007; John D. Co-Reyes; Rishabh Agarwal; et al. (NeurIPS, 2023)

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 (ECCV (89), 2024)