Imitation Learning

All Publication Topics Autonomous Driving Autonomous Exploration Benchmarks Compression Computer Vision First-Person Video Forecasting Generative Models Imitation Learning Intrinsic Control Machine Learning Offroad Navigation Paper Award Planning Reinforcement Learning Reward Learning Robotics

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.
The Waymo Open Sim Agents Challenge.
Advances in Neural Information Processing Systems, (NeurIPS), 2023.

Autonomous Driving Benchmarks Computer Vision Forecasting Imitation Learning Machine Learning Robotics

Hybrid imitative planning with geometric and predictive costs in off-road environments

Nitish Dashora, Daniel Shin, Dhruv Shah, Henry Leopold, David Fan, Ali Agha-Mohammadi, Nicholas Rhinehart, Sergey Levine.
Hybrid imitative planning with geometric and predictive costs in off-road environments.
International Conference on Robotics and Automation, (ICRA), 2022.

Imitation Learning Machine Learning Planning Offroad Navigation Robotics

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.
Contingencies from observations: Tractable contingency planning with learned behavior models.
IEEE International Conference on Robotics and Automation, (ICRA), 2021.

Autonomous Driving Computer Vision Forecasting Imitation Learning Machine Learning Planning Robotics

Parrot: Data-driven behavioral priors for reinforcement learning

Avi Singh, Huihan Liu, Gaoyue Zhou, Albert Yu, Nicholas Rhinehart, Sergey Levine.
Parrot: Data-driven behavioral priors for reinforcement learning.
International Conference on Learning Representations, (ICLR), 2020.

Generative Models Imitation Learning Machine Learning Robotics

Deep Imitative Models for Flexible Inference, Planning, and Control

Nicholas Rhinehart, Rowan McAllister, Sergey Levine.
Deep Imitative Models for Flexible Inference, Planning, and Control.
International Conference on Learning Representations, (ICLR), 2020.

Autonomous Driving Computer Vision Forecasting Generative Models Imitation Learning Planning Reward Learning

Can autonomous vehicles identify, recover from, and adapt to distribution shifts?

Angelos Filos, Panagiotis Tigkas, Rowan McAllister, Nicholas Rhinehart, Sergey Levine, Yarin Gal.
Can autonomous vehicles identify, recover from, and adapt to distribution shifts?.
International Conference on Machine Learning, (ICML), 2020.

Autonomous Driving Computer Vision Imitation Learning Planning Machine Learning

Jointly Forecasting and Controlling Behavior by Learning from High-Dimensional Data

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

Autonomous Driving Forecasting Generative Models Imitation Learning Planning Reinforcement Learning Reward Learning

Generative Hybrid Representations for Activity Forecasting with No-Regret Learning

Jiaqi Guan, Ye Yuan, Kris M Kitani, Nicholas Rhinehart.
Generative Hybrid Representations for Activity Forecasting with No-Regret Learning.
Conference on Computer Vision and Pattern Recognition, (CVPR), 2019.

Computer Vision Imitation Learning First-Person Video Forecasting Generative Models Machine Learning

Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information

Arjun Sharma, Mohit Sharma, Nicholas Rhinehart, Kris M Kitani.
Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information.
International Conference on Learning Representations (ICLR), (ICLR), 2019.

Imitation Learning Robotics

R2P2: A Reparameterized Pushforward Policy for Diverse, Precise Generative Path Forecasting

Nicholas Rhinehart, Kris M. Kitani, Paul Vernaza.
R2P2: A Reparameterized Pushforward Policy for Diverse, Precise Generative Path Forecasting.
Proceedings of the European Conference on Computer Vision, (ECCV), 2018.

Autonomous Driving Imitation Learning Forecasting Generative Models Machine Learning Reward Learning

Learning Neural Parsers with Deterministic Differentiable Imitation Learning

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

Imitation Learning Machine Learning

Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning

Xinlei Pan, Eshed Ohn-Bar, Nicholas Rhinehart, Yan Xu, Yilin Shen, Kris M. Kitani.
Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning.
Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, (AAMAS), 2018.

Imitation Learning Machine Learning Reward Learning

First-Person Activity Forecasting from Video with Online Inverse Reinforcement Learning

Nicholas Rhinehart, Kris Kitani.
First-Person Activity Forecasting from Video with Online Inverse Reinforcement Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence, (PAMI), 2018.

Imitation Learning Forecasting Machine Learning Reward Learning

Predictive-state decoders: Encoding the future into recurrent networks

Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots, Kris M. Kitani, J. A. Bagnell.
Predictive-state decoders: Encoding the future into recurrent networks.
Advances in Neural Information Processing Systems, (NeurIPS), 2017.

Imitation Learning Forecasting Machine Learning Reinforcement Learning

First-Person Activity Forecasting with Online Inverse Reinforcement Learning

Nicholas Rhinehart, Kris M. Kitani.
First-Person Activity Forecasting with Online Inverse Reinforcement Learning.
The IEEE International Conference on Computer Vision, (ICCV), 2017.
Best Paper Honorable Mention

Paper Award Imitation Learning Reward Learning First-Person Video

Visual chunking: A list prediction framework for region-based object detection

Nicholas Rhinehart, Jiaji Zhou, Martial Hebert, J Andrew Bagnell.
Visual chunking: A list prediction framework for region-based object detection.
IEEE International Conference on Robotics and Automation, (ICRA), 2015.

Computer Vision Imitation Learning Machine Learning