Visual chunking: A list prediction framework for region-based object detection. - Nicholas Rhinehart; Jiaji Zhou; Martial Hebert; J. Andrew Bagnell (ICRA, 2015)

Learning Action Maps of Large Environments via First-Person Vision. - Nicholas Rhinehart; Kris Makoto Kitani (CVPR, 2016)

Online Semantic Activity Forecasting with DARKO. - Nicholas Rhinehart; Kris M. Kitani (CoRR, 2016)

First-Person Activity Forecasting with Online Inverse Reinforcement Learning. - Nicholas Rhinehart; Kris M. Kitani (ICCV, 2017)

Predictive-State Decoders: Encoding the Future into Recurrent Networks. - Arun Venkatraman; Nicholas Rhinehart; Wen Sun 002; Lerrel Pinto; Martial Hebert; Byron Boots; Kris M. Kitani; James Andrew Bagnell (NIPS, 2017)

Learning Neural Parsers with Deterministic Differentiable Imitation Learning. - Tanmay Shankar; Nicholas Rhinehart; Katharina Muelling; Kris M. Kitani (CoRL, 2018)

r2p2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting. - Nicholas Rhinehart; Kris M. Kitani; Paul Vernaza (ECCV (13), 2018)

N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning. - Anubhav Ashok; Nicholas Rhinehart; Fares Beainy; Kris M. Kitani (ICLR (Poster), 2018)

Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning. - Xinlei Pan; Eshed Ohn-Bar; Nicholas Rhinehart; Yan Xu; Yilin Shen; Kris M. Kitani (CoRR, 2018)

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

Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information. - Mohit Sharma 001; Arjun Sharma; Nicholas Rhinehart; Kris M. Kitani (ICLR (Poster), 2019)

Jointly Forecasting and Controlling Behavior by Learning from High-Dimensional Data. - Nicholas Rhinehart (2019)

Inverting the Pose Forecasting Pipeline with SPF2: Sequential Pointcloud Forecasting for Sequential Pose Forecasting. - Xinshuo Weng; Jianren Wang; Sergey Levine; Kris Kitani; Nicholas Rhinehart (CoRL, 2020)

Generative Hybrid Representations for Activity Forecasting With No-Regret Learning. - Jiaqi Guan; Ye Yuan 007; Kris M. Kitani; Nicholas Rhinehart (CVPR, 2020)

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)

Unsupervised Sequence Forecasting of 100,000 Points for Unsupervised Trajectory Forecasting. - Xinshuo Weng; Jianren Wang; Sergey Levine; Kris Kitani; Nicholas Rhinehart (CoRR, 2020)

First-Person Activity Forecasting from Video with Online Inverse Reinforcement Learning. - Nicholas Rhinehart; Kris M. Kitani (IEEE Trans. Pattern Anal. Mach. Intell., 2020)

Rapid Exploration for Open-World Navigation with Latent Goal Models. - Dhruv Shah; Benjamin Eysenbach; Nicholas Rhinehart; Sergey Levine (CoRL, 2021)

SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments. - Glen Berseth; Daniel Geng; Coline Manon Devin; Nicholas Rhinehart; Chelsea Finn; Dinesh Jayaraman; Sergey Levine (ICLR, 2021)

Conservative Safety Critics for Exploration. - Homanga Bharadhwaj; Aviral Kumar; Nicholas Rhinehart; Sergey Levine; Florian Shkurti; Animesh Garg (ICLR, 2021)

Parrot: Data-Driven Behavioral Priors for Reinforcement Learning. - Avi Singh; Huihan Liu; Gaoyue Zhou; Albert Yu 002; Nicholas Rhinehart; 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)

ViNG: Learning Open-World Navigation with Visual Goals. - Dhruv Shah; Benjamin Eysenbach; Gregory Kahn; Nicholas Rhinehart; Sergey Levine (ICRA, 2021)

Information is Power: Intrinsic Control via Information Capture. - Nicholas Rhinehart; Jenny Wang; Glen Berseth; John D. Co-Reyes; Danijar Hafner; Chelsea Finn; Sergey Levine (NeurIPS, 2021)

RECON: Rapid Exploration for Open-World Navigation with Latent Goal Models. - Dhruv Shah; Benjamin Eysenbach; Nicholas Rhinehart; Sergey Levine (CoRR, 2021)

Explore and Control with Adversarial Surprise. - Arnaud Fickinger; Natasha Jaques; Samyak Parajuli; Michael Chang 003; Nicholas Rhinehart; Glen Berseth; Stuart Russell 001; Sergey Levine (CoRR, 2021)

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)

Offline Reinforcement Learning for Visual Navigation. - Dhruv Shah; Arjun Bhorkar; Hrishit Leen; Ilya Kostrikov; Nicholas Rhinehart; Sergey Levine (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)

Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments. - Nitish Dashora; Daniel Shin; Dhruv Shah; Henry A. Leopold; David D. Fan; Ali-Akbar Agha-Mohammadi; Nicholas Rhinehart; Sergey Levine (ICRA, 2022)

The Waymo Open Sim Agents Challenge. - Nico Montali; John Lambert; Paul Mougin; Alex Kuefler; Nicholas Rhinehart; Michelle Li; Cole Gulino; Tristan Emrich; Zoey Yang; Shimon Whiteson; Brandyn White; Dragomir Anguelov (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)

DR-MPC: Deep Residual Model Predictive Control for Real-World Social Navigation. - James R. Han; Hugues Thomas; Jian Zhang 050; Nicholas Rhinehart; Timothy D. Barfoot (IEEE Robotics Autom. Lett., 2025)

Towards foundational LiDAR world models with efficient latent flow matching. - Tianran Liu; Shengwen Zhao; Nicholas Rhinehart (CoRR, 2025)

Residual Reward Models for Preference-based Reinforcement Learning. - Chenyang Cao; Miguel Rogel Garcia; Mohamed Nabail; Xueqian Wang; Nicholas Rhinehart (CoRR, 2025)