Generative Models

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

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

Jiezhi Yang, Khushi Desai, Charles Packer, Harshil Bhatia, Nicholas Rhinehart, Rowan McAllister, Joseph Gonzalez.
CARFF: Conditional Auto-encoded Radiance Field for 3D Scene Forecasting.
European Conference on Computer Vision, (ECCV), 2024.

Autonomous Driving Computer Vision Forecasting Generative Models Machine Learning Robotics

Is anyone there? learning a planner contingent on perceptual uncertainty

Charles Packer, Nicholas Rhinehart, Rowan McAllister, Matthew a Wright, Xin Wang, Jeff He, Sergey Levine, Joseph E Gonzalez.
Is anyone there? learning a planner contingent on perceptual uncertainty.
Conference on Robot Learning, (CoRL), 2023.

Autonomous Driving Computer Vision Forecasting Generative Models Machine Learning Planning Robotics

S2Net: Stochastic Sequential Pointcloud Forecasting

Xinshuo Weng, Junyu Nan, Kuan-Hui Lee, Rowan McAllister, Adrien Gaidon, Nicholas Rhinehart, Kris Kitani.
S2Net: Stochastic Sequential Pointcloud Forecasting.
European Conference on Computer Vision, (ECCV), 2022.

Autonomous Driving Forecasting Generative Models Machine Learning

Inverting the pose forecasting pipeline with SPF2: Sequential pointcloud forecasting for sequential pose forecasting

Xinshuo Weng, Jianren Wang, Sergey Levine, Kris Kitani, Nicholas Rhinehart.
Inverting the pose forecasting pipeline with SPF2: Sequential pointcloud forecasting for sequential pose forecasting.
Conference on robot learning, (CoRL), 2021.

Autonomous Driving Computer Vision Generative Models Machine Learning Robotics

Information is power: Intrinsic control via information capture

Nicholas Rhinehart, Jenny Wang, Glen Berseth, John Co-Reyes, Danijar Hafner, Chelsea Finn, Sergey Levine.
Information is power: Intrinsic control via information capture.
Advances in Neural Information Processing Systems, (NeurIPS), 2021.

Autonomous Exploration Intrinsic Control Generative Models Reinforcement Learning

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

PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings

Nicholas Rhinehart, Rowan McAllister, Kris Kitani, Sergey Levine.
PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings.
Proceedings of the IEEE International Conference on Computer Vision, (ICCV), 2019.
Best Paper Award @ ICML 2019 Workshop on AI for Autonomous Driving

Paper Award Forecasting Generative Models Autonomous Driving

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

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