Publications


Preprints

Lifelong Robotic Reinforcement Learning by Retaining Experiences
Annie Xie, Chelsea Finn
Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets
Frederik Ebert*, Yanlai Yang*, Karl Schmeckpeper, Bernadette Bucher, Georgios Georgakis, Kostas Daniilidis, Chelsea Finn, Sergey Levine
Meta-Learning with Fewer Tasks through Task Interpolation
Huaxiu Yao, Linjun Zhang, Chelsea Finn
ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback
Mike Wu, Noah Goodman, Chris Piech, Chelsea Finn

2021

Noether Networks: Meta-Learning Useful Conserved Quantities
Ferran Alet*, Dylan Doblar*, Allan Zhou, Joshua B. Tenenbaum, Kenji Kawaguchi, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 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
Neural Information Processing Systems (NeurIPS), 2021
Meta-learning with an Adaptive Task Scheduler
Huaxiu Yao*, Yu Wang*, Ying Wei, Peilin Zhao, Mehrdad Mahdavi, Defu Lian, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2021
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning
Tianhe Yu*, Aviral Kumar*, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2021
COMBO: Conservative Offline Model-Based Policy Optimization
Tianhe Yu*, Aviral Kumar*, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2021
Visual Adversarial Imitation Learning using Variational Models
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2021
Autonomous Reinforcement Learning via Subgoal Curricula
Archit Sharma, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2021
Efficiently Identifying Task Groupings for Multi-Task Learning
Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2021 (Spotlight)
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift
Marvin Zhang*, Henrik Marklund*, Nikita Dhawan*, Abhishek Gupta, Sergey Levine, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2021
Differentiable Annealed Importance Sampling and the Perils of Gradient Noise
Guodong Zhang, Kyle Hsu, Jianing Li, Chelsea Finn, Roger Grosse
Neural Information Processing Systems (NeurIPS), 2021
Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks
Bohan Wu, Suraj Nair, Li Fei-Fei, Chelsea Finn
Conference on Robot Learning (CoRL), 2021
A Workflow for Offline Model-Free Robotic Reinforcement Learning
Aviral Kumar*, Anikait Singh*, Stephen Tian, Chelsea Finn, Sergey Levine
Conference on Robot Learning (CoRL), 2021
Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation
Suraj Nair, Eric Mitchell, Kevin Chen, Brian Ichter, Silvio Savarese, Chelsea Finn
Conference on Robot Learning (CoRL), 2021
MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale
Dmitry Kalashnikov*, Jake Varley*, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, Karol Hausman
Conference on Robot Learning (CoRL), 2021
Learning Generalizable Robotic Reward Functions from In-The-Wild Human Videos
Annie S. Chen, Suraj Nair, Chelsea Finn
Robotics: Science and Systems (RSS), 2021
Deep Reinforcement Learning amidst Continual Structured Non-Stationarity
Annie Xie, James Harrison, Chelsea Finn
International Conference on Machine Learning (ICML), 2021
Offline Meta-Reinforcement Learning with Advantage Weighting
Eric Mitchell, Rafael Rafailov, Xue Bin Peng, Sergey Levine, Chelsea Finn.
International Conference on Machine Learning (ICML), 2021
Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills
Yevgen Chebotar, Karol Hausman, Yao Lu, Ted Xiao, Dmitry Kalashnikov, Jake Varley, Alex Irpan, Ryan Julian, Chelsea Finn, Sergey Levine
International Conference on Machine Learning (ICML), 2021
Just Train Twice: Improving Group Robustness without Training Group Information
Evan Z. Liu*, Behzad Haghgoo*, Annie S. Chen*, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn
International Conference on Machine Learning (ICML), 2021 (Long Talk)
Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices
Evan Z. Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn
International Conference on Machine Learning (ICML), 2021
WILDS: A Benchmark of in the Wild Distribution Shifts
Pang Wei Koh*, Shiori Sagawa*, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, Percy Liang
International Conference on Machine Learning (ICML), 2021 (Long Talk)
Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction
Bohan Wu, Suraj Nair, Roberto Martín-Martín, Li Fei-Fei, Chelsea Finn
Conference on Computer Vision and Pattern Recognition (CVPR), 2021
Offline Reinforcement Learning from Images with Latent Space Models
Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn
Learning for Decision Making and Control (L4DC), 2021
Batch Exploration with Examples for Scalable Robotic Reinforcement Learning
Annie S. Chen, HyunJi Nam, Suraj Nair, Chelsea Finn
Robotics and Automation Letters (RA-L). International Conference on Robotics and Automation (ICRA), 2021
Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones
Brijen Thananjeyan, Ashwin Balakrishna, Suraj Nair, Michael Luo, Krishnan Srinivasan, Minho Hwang, Joseph E. Gonzalez, Julian Ibarz, Chelsea Finn, Ken Goldberg
Robotics and Automation Letters (RA-L). International Conference on Robotics and Automation (ICRA)
How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned
Julian Ibarz, Jie Tan, Chelsea Finn, Mrinal Kalakrishnan, Peter Pastor, Sergey Levine
International Journal of Robotics Research (IJRR), 2021
Meta-Learning Symmetries by Reparameterization
Allan Zhou, Tom Knowles, Chelsea Finn
International Conference on Learning Representations (ICLR), 2021
Model-Based Visual Planning with Self-Supervised Functional Distances
Stephen Tian, Suraj Nair, Frederik Ebert, Sudeep Dasari, Ben Eysenbach, Sergey Levine, Chelsea Finn
International Conference on Learning Representations (ICLR), 2021 (Spotlight)
SMiRL: Surprise Minimizing RL in Dynamic Environments
Glen Berseth, Daniel Geng, Coline Devin, Chelsea Finn, Dinesh Jayaraman, Sergey Levine
International Conference on Learning Representations (ICLR), 2021 (Oral)

2020

Reinforcement Learning with Videos: Combining Offline Observations with Interaction
Karl Schmeckpeper, Oleh Rybkin, Kostas Daniilidis, Sergey Levine, Chelsea Finn
Conference on Robot Learning (CoRL), 2020 (Oral)
Learning Latent Representations to Influence Multi-Agent Interaction
Annie Xie, Dylan Losey, Ryan Tolsma, Dorsa Sadigh, Chelsea Finn
Conference on Robot Learning (CoRL), 2020 (Oral, Best Paper Award)
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning
Ryan Julian, Benjamin Swanson, Gaurav Sukhatme, Sergey Levine, Chelsea Finn, Karol Hausman
Conference on Robot Learning (CoRL), 2020
One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL
Saurabh Kumar, Aviral Kumar, Sergey Levine, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2020
Continual Learning of Control Primitives: Skill Discovery via Reset-Games
Kelvin Xu, Siddharth Verma, Chelsea Finn, Sergey Levine
Neural Information Processing Systems (NeurIPS)
Gradient Surgery for Multi-Task Learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2020
Weakly-Supervised Reinforcement Learning for Controllable Behavior
Lisa Lee, Ben Eysenbach, Ruslan Salakhutdinov, Shixiang Gu, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2020
MOPO: Model-based Offline Policy Optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, Tengyu Ma
Neural Information Processing Systems (NeurIPS)
Continuous Meta-Learning without Tasks
James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone
Neural Information Processing Systems (NeurIPS), 2020
Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors
Karl Pertsch, Oleh Rybkin, Frederik Ebert, Chelsea Finn, Dinesh Jayaraman, Sergey Levine
Neural Information Processing Systems (NeurIPS), 2020
Learning Predictive Models From Observation and Interaction
Karl Schmeckpeper, Annie Xie, Oleh Rybkin, Stephen Tian, Kostas Daniilidis, Sergey Levine, Chelsea Finn
European Conference on Computer Vision (ECCV), 2020
Goal-Aware Prediction: Learning to Model What Matters
Suraj Nair, Silvio Savarese, Chelsea Finn
International Conference on Machine Learning (ICML), 2020
Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings
Jesse Zhang, Brian Cheung, Chelsea Finn, Sergey Levine, Dinesh Jayaraman
International Conference on Machine Learning (ICML), 2020
Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning
Xingyou Song, Yuxiang Yang, Krzysztof Choromanski, Ken Caluwaerts, Wenbo Gao, Chelsea Finn, Jie Tan
International Conference on Intelligent Robots and Systems (IROS), 2020
Scalable Multi-Task Imitation Learning with Autonomous Improvement
Avi Singh, Eric Jang, Daniel Kappler, Mohi Khansari, Murtaza Dalal, Alex Irpan, Sergey Levine, Mohi Khansari, Chelsea Finn
International Conference on Robotics and Automation (ICRA), 2020
Time Reversal as Self-Supervision
Suraj Nair, Mohammad Babaeizadeh, Chelsea Finn, Sergey Levine, Vikash Kumar
International Conference on Robotics and Automation (ICRA), 2020
OmniTact: Compact Multi-Directional Optical Tactile Sensor for Robotic Manipulation
Akhil Padmanabha, Frederik Ebert, Stephen Tian, Roberto Calandra, Sergey Levine
International Conference on Robotics and Automation (ICRA), 2020
Meta-Learning without Memorization
Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn
International Conference on Learning Representations (ICLR), 2020 (Spotlight)
Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards
Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn
International Conference on Learning Representations (ICLR), 2020
Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation
Suraj Nair, Chelsea Finn
International Conference on Learning Representations (ICLR), 2020
Model-Based Reinforcement Learning for Atari
Lukasz Kaiser, Mohammad Babaeizadeh, Piotr Milos, Blazej Osinski, Roy Campbell, Konrad Czechowski, Dumitru Erhan, Chelsea Finn, Piotr Kozakowski, Sergey Levine, Afroz Mohiuddin, Ryan Sepassi, George Tucker, Henryk Michalewski
International Conference on Learning Representations (ICLR), 2020 (Spotlight)
VideoFlow: A Flow-Based Generative Model for Video
Manoj Kumar, Mohammad Babaeizadeh, Dumitru Erhan, Chelsea Finn, Sergey Levine, Laurent Dinh, Durk Kingma
International Conference on Learning Representations (ICLR), 2020
Learning to Interactively Learn and Assist
Mark Woodward, Chelsea Finn, Karol Hausman
AAAI Conference on Artificial Intelligence, 2020 (Oral)

2019

RoboNet: Large-Scale Multi-Robot Learning
Sudeep Dasari, Frederik Ebert, Stephen Tian, Suraj Nair, Bernadette Bucher, Karl Schmeckpeper, Siddharth Singh, Sergey Levine, Chelsea Finn
Conference on Robot Learning (CoRL), 2019
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Karol Hausman, Sergey Levine, Chelsea Finn
Conference on Robot Learning (CoRL), 2019
Unsupervised Curricula for Visual Meta-Reinforcement Learning
Allan Jabri, Kyle Hsu, Abhishek Gupta, Ben Eysenbach, Sergey Levine, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2019 (Spotlight)
Meta-Learning with Implicit Gradients
Aravind Rajeswaran, Chelsea Finn, Sham Kakade, Sergey Levine
Neural Information Processing Systems (NeurIPS), 2019
Language as an Abstraction for Hierarchical Reinforcement Learning
YiDing Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2019
Guided Meta-Policy Search
Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn
Neural Information Processing Systems (NeurIPS) (Spotlight)
Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon
Neural Information Processing Systems (NeurIPS), 2019
One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks
Tianhe Yu, Pieter Abbeel, Sergey Levine, Chelsea Finn
International Conference on Intelligent Robots and Systems (IROS), 2019
End-to-End Robotic Reinforcement Learning without Reward Engineering
Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, Sergey Levine
Robotics: Science and Systems (RSS), 2019
Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight
Annie Xie, Frederik Ebert, Sergey Levine, Chelsea Finn
Robotics: Science and Systems (RSS), 2019
Unsupervised Visuomotor Control Through Distributional Planning Networks
Tianhe Yu, Gleb Shevchuk, Dorsa Sadigh, Chelsea Finn
Robotics: Science and Systems (RSS), 2019
Online Meta-Learning
Chelsea Finn*, Aravind Rajeswaran*, Sham Kakade, Sergey Levine
International Conference on Machine Learning (ICML), 2019
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables
Kate Rakelly, Aurick Zhou, Deirdre Quillen, Chelsea Finn, Sergey Levine
International Conference on Machine Learning (ICML), 2019
Learning a Prior over Intent via Meta-Inverse Reinforcement Learning
Kelvin Xu, Ellis Ratner, Anca Dragan, Sergey Levine, Chelsea Finn
International Conference on Machine Learning (ICML), 2019
Manipulation by Feel: Touch-Based Control with Deep Predictive Models
Stephen Tian*, Frederik Ebert*, Dinesh Jayaraman, Mayur Mudigonda, Chelsea Finn, Roberto Calandra, Sergey Levine
International Conference on Robotics and Automation (ICRA), 2019
NoRML: No-Reward Meta Learning
Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Jie Tan, Chelsea Finn
International Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2019
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
Anusha Nagabandi*, Ignasi Clavera*, Simin Liu, Ron Fearing, Pieter Abbeel, Sergey Levine, Chelsea Finn
International Conference on Learning Representations (ICLR), 2019
Unsupervised Learning via Meta-Learning
Kyle Hsu, Sergey Levine, Chelsea Finn
International Conference on Learning Representations (ICLR), 2019
Reasoning About Physical Interactions with Object-Oriented Prediction and Planning
Michael Janner, Sergey Levine, Bill Freeman, Josh Tenenbaum, Chelsea Finn, Jiajun Wu
International Conference on Learning Representations (ICLR), 2019
Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL
Anusha Nagabandi, Chelsea Finn, Sergey Levine
International Conference on Learning Representations (ICLR), 2019

2018

Probabilistic Model-Agnostic Meta-Learning
Chelsea Finn*, Kelvin Xu*, Sergey Levine
Neural Information Processing Systems (NeurIPS), 2018
Learning to Learn with Gradients
Chelsea Finn
PhD Thesis, 2018
Few-Shot Goal Inference for Visuomotor Learning and Planning
Annie Xie, Avi Singh, Sergey Levine, Chelsea Finn
Conference on Learning (CoRL), 2018
Robustness via Retrying: Closed-Loop Robotic Manipulation via Self-Supervised Learning
Frederik Ebert, Sudeep Dasari, Alex Lee, Sergey Levine, Chelsea Finn
Conference on Learning (CoRL), 2018
Universal Planning Networks
Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn
International Conference on Machine Learning (ICML), 2018
One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning
Tianhe Yu*, Chelsea Finn*, Annie Xie, Sudeep Dasari, Pieter Abbeel, Sergey Levine
Robotics: Science and Systems (RSS), 2018
Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm
Chelsea Finn, Sergey Levine
International Conference on Learning Representations (ICLR), 2018
Recasting Gradient-Based Meta-Learning as Hierarchical Bayes
Erin Grant, Chelsea Finn, Sergey Levine , Trevor Darrell, Tom Griffiths
International Conference on Learning Representations (ICLR), 2018
Stochastic Variational Video Prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy Campbell, Sergey Levine
International Conference on Learning Representations (ICLR), 2018
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods
Deirdre Quillen*, Eric Jang*, Ofir Nachum*, Chelsea Finn, Julian Ibarz , Sergey Levine
International Conference on Robotics and Automation (ICRA), 2018

2017

One-Shot Visual Imitation Learning via Meta-Learning
Chelsea Finn*, Tianhe Yu*, Tianhao Zhang, Pieter Abbeel, Sergey Levine
Conference on Robot Learning (CoRL), 2017 (Long Talk)
Self-Supervised Visual Planning with Temporal Skip Connections
Frederik Ebert, Chelsea Finn, Alex Lee, Sergey Levine
Conference on Robot Learning (CoRL), 2017 (Long Talk)
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn, Pieter Abbeel, Sergey Levine
International Conference on Machine Learning (ICML), 2017
Generalizing Skills with Semi-Supervised Reinforcement Learning
Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine
International Conference on Learning Representations (ICLR), 2017
Deep Visual Foresight for Planning Robot Motion
Chelsea Finn, Sergey Levine
International Conference on Robotics and Automation (ICRA), 2017 (Best Cognitive Robotics Paper Finalist)
Reset-Free Guided Policy Search: Efficient Deep Reinforcement Learning with Stochastic Initial States
William Montgomery*, Anurag Ajay*, Chelsea Finn, Pieter Abbeel, Sergey Levine
International Conference on Robotics and Automation (ICRA), 2017

2016

A Connection Between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models
Chelsea Finn*, Paul Christiano*, Pieter Abbeel, Sergey Levine
NIPS Workshop on Adversarial Training, 2016
Active One-Shot Learning
Mark Woodward, Chelsea Finn
NIPS Deep Reinforcement Learning Workshop, 2016
Unsupervised Learning for Physical Interaction through Video Prediction
Chelsea Finn, Ian Goodfellow, Sergey Levine
Neural Information Processing Systems (NIPS), 2016
Adapting Deep Visuomotor Representations with Weak Pairwise Constraints
Eric Tzeng, Coline Devin, Judy Hoffman, Chelsea Finn, Pieter Abbeel, Sergey Levine, Kate Saenko, Trevor Darrell
Workshop on the Algorithmic Foundations of Robotics (WAFR), 2016
Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
Chelsea Finn, Sergey Levine, Pieter Abbeel
International Conference on Machine Learning (ICML), 2016
End-to-End Training of Deep Visuomotor Policies
Sergey Levine*, Chelsea Finn*, Trevor Darrell, Pieter Abbeel
Journal of Machine Learning Research (JMLR), 2016
Learning Deep Neural Network Policies with Continuous Memory States
Marvin Zhang, Zoe McCarthy, Chelsea Finn, Sergey Levine, Pieter Abbeel
International Conference on Robotics and Automation (ICRA), 2016
Deep Spatial Autoencoders for Visuomotor Learning
Chelsea Finn, Xin Yu Tan, Yan Duan, Trevor Darrell, Sergey Levine, Pieter Abbeel
International Conference on Robotics and Automation (ICRA), 2016