Nicholas Rhinehart

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 47

Top Topics

Recurrent Networks

Reinforcement Learning

Learning From Experience

Visual Navigation

Top Venues

Publications

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 (2024)
Nico Montali, John Lambert, Paul Mougin, Alex Kuefler, Nicholas Rhinehart, Michelle Li, Cole Gulino, Tristan Emrich, Zoey Yang, Shimon Whiteson, Brandyn White, Dragomir Anguelov
The Waymo Open Sim Agents Challenge. NeurIPS (2023)
Nitish Dashora, Daniel Shin, Dhruv Shah, Henry A. Leopold, David D. Fan, Ali-Akbar Agha-Mohammadi, Nicholas Rhinehart, Sergey Levine
Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments. ICRA (2022)
Xinshuo Weng, Junyu Nan, Kuan-Hui Lee, Rowan McAllister, Adrien Gaidon, Nicholas Rhinehart, Kris M. Kitani
S2Net: Stochastic Sequential Pointcloud Forecasting. ECCV (27) (2022)
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)
Dhruv Shah, Arjun Bhorkar, Hrishit Leen, Ilya Kostrikov, Nicholas Rhinehart, Sergey Levine
Offline Reinforcement Learning for Visual Navigation. CoRL (2022)
Nicholas Rhinehart, Jenny Wang, Glen Berseth, John D. Co-Reyes, Danijar Hafner, Chelsea Finn, Sergey Levine
Information is Power: Intrinsic Control via Information Capture. NeurIPS (2021)
Nitish Dashora, Daniel Shin, Dhruv Shah, Henry A. Leopold, David D. Fan, Ali-Akbar Agha-Mohammadi, Nicholas Rhinehart, Sergey Levine
Hybrid Imitative Planning with Geometric and Predictive Costs in Off-road Environments. CoRR (2021)
Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, Sergey Levine
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning. ICLR (2021)
Glen Berseth, Daniel Geng, Coline Manon Devin, Nicholas Rhinehart, Chelsea Finn, Dinesh Jayaraman, Sergey Levine
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments. ICLR (2021)
Arnaud Fickinger, Natasha Jaques, Samyak Parajuli, Michael Chang, Nicholas Rhinehart, Glen Berseth, Stuart Russell, Sergey Levine
Explore and Control with Adversarial Surprise. CoRR (2021)
Dhruv Shah, Benjamin Eysenbach, Nicholas Rhinehart, Sergey Levine
RECON: Rapid Exploration for Open-World Navigation with Latent Goal Models. CoRR (2021)
Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine
ViNG: Learning Open-World Navigation with Visual Goals. ICRA (2021)
Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg
Conservative Safety Critics for Exploration. ICLR (2021)
Dhruv Shah, Benjamin Eysenbach, Nicholas Rhinehart, Sergey Levine
Rapid Exploration for Open-World Navigation with Latent Goal Models. CoRL (2021)
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 (2021)
Nicholas Rhinehart, Jenny Wang, Glen Berseth, John D. Co-Reyes, Danijar Hafner, Chelsea Finn, Sergey Levine
Information is Power: Intrinsic Control via Information Capture. CoRR (2021)
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)
Jiaqi Guan, Ye Yuan, Kris M. Kitani, Nicholas Rhinehart
Generative Hybrid Representations for Activity Forecasting With No-Regret Learning. CVPR (2020)
Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, Sergey Levine
Parrot: Data-Driven Behavioral Priors for Reinforcement Learning. CoRR (2020)
Angelos Filos, Panagiotis Tigas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal
Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts? CoRR (2020)
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)
Nicholas Rhinehart, Rowan McAllister, Sergey Levine
Deep Imitative Models for Flexible Inference, Planning, and Control. ICLR (2020)
Homanga Bharadhwaj, Aviral Kumar, Nicholas Rhinehart, Sergey Levine, Florian Shkurti, Animesh Garg
Conservative Safety Critics for Exploration. CoRR (2020)
Nicholas Rhinehart, Kris M. Kitani
First-Person Activity Forecasting from Video with Online Inverse Reinforcement Learning. IEEE Trans. Pattern Anal. Mach. Intell. 42 (2) (2020)
Dhruv Shah, Benjamin Eysenbach, Gregory Kahn, Nicholas Rhinehart, Sergey Levine
ViNG: Learning Open-World Navigation with Visual Goals. CoRR (2020)
Xinshuo Weng, Jianren Wang, Sergey Levine, Kris Kitani, Nicholas Rhinehart
Unsupervised Sequence Forecasting of 100,000 Points for Unsupervised Trajectory Forecasting. CoRR (2020)
Xinshuo Weng, Jianren Wang, Sergey Levine, Kris Kitani, Nicholas Rhinehart
Inverting the Pose Forecasting Pipeline with SPF2: Sequential Pointcloud Forecasting for Sequential Pose Forecasting. CoRL (2020)
Nicholas Rhinehart, Rowan McAllister, Kris Kitani, Sergey Levine
PRECOG: PREdiction Conditioned on Goals in Visual Multi-Agent Settings. ICCV (2019)
Nicholas Rhinehart, Rowan McAllister, Kris M. Kitani, Sergey Levine
PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings. CoRR (2019)
Jiaqi Guan, Ye Yuan, Kris M. Kitani, Nicholas Rhinehart
Generative Hybrid Representations for Activity Forecasting with No-Regret Learning. CoRR (2019)
Mohit Sharma, Arjun Sharma, Nicholas Rhinehart, Kris M. Kitani
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information. ICLR (Poster) (2019)
Arjun Sharma, Mohit Sharma, Nicholas Rhinehart, Kris M. Kitani
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information. CoRR (2018)
Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, Kris M. Kitani
N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning. ICLR (Poster) (2018)
Tanmay Shankar, Nicholas Rhinehart, Katharina Muelling, Kris M. Kitani
Learning Neural Parsers with Deterministic Differentiable Imitation Learning. CoRR (2018)
Xinlei Pan, Eshed Ohn-Bar, Nicholas Rhinehart, Yan Xu, Yilin Shen, Kris M. Kitani
Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning. CoRR (2018)
Tanmay Shankar, Nicholas Rhinehart, Katharina Muelling, Kris M. Kitani
Learning Neural Parsers with Deterministic Differentiable Imitation Learning. CoRL (2018)
Nicholas Rhinehart, Rowan McAllister, Sergey Levine
Deep Imitative Models for Flexible Inference, Planning, and Control. CoRR (2018)
Nicholas Rhinehart, Kris M. Kitani, Paul Vernaza
r2p2: A ReparameteRized Pushforward Policy for Diverse, Precise Generative Path Forecasting. ECCV (13) (2018)
Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, J. Andrew Bagnell
Predictive-State Decoders: Encoding the Future into Recurrent Networks. CoRR (2017)
Nicholas Rhinehart, Kris M. Kitani
First-Person Activity Forecasting with Online Inverse Reinforcement Learning. ICCV (2017)
Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, James Andrew Bagnell
Predictive-State Decoders: Encoding the Future into Recurrent Networks. NIPS (2017)
Anubhav Ashok, Nicholas Rhinehart, Fares Beainy, Kris M. Kitani
N2N Learning: Network to Network Compression via Policy Gradient Reinforcement Learning. CoRR (2017)
Nicholas Rhinehart, Kris M. Kitani
Learning Action Maps of Large Environments via First-Person Vision. CoRR (2016)
Nicholas Rhinehart, Kris Makoto Kitani
Learning Action Maps of Large Environments via First-Person Vision. CVPR (2016)
Nicholas Rhinehart, Kris M. Kitani
Online Semantic Activity Forecasting with DARKO. CoRR (2016)
Nicholas Rhinehart, Jiaji Zhou, Martial Hebert, J. Andrew Bagnell
Visual chunking: A list prediction framework for region-based object detection. ICRA (2015)
Nicholas Rhinehart, Jiaji Zhou, Martial Hebert, J. Andrew Bagnell
Visual Chunking: A List Prediction Framework for Region-Based Object Detection. CoRR (2014)