Chelsea Finn
cbfinn at cs dot stanford dot edu

I am an Assistant Professor in Computer Science and Electrical Engineering at Stanford University. My lab, IRIS, studies intelligence through robotic interaction at scale, and is affiliated with SAIL and the Statistical ML Group. I also spend time at Google as a part of the Google Brain team.

I am interested in the capability of robots and other agents to develop broadly intelligent behavior through learning and interaction.

Previously, I completed my Ph.D. in computer science at UC Berkeley and my B.S. in electrical engineering and computer science at MIT.

Prospective students and post-docs, please read this before contacting me.

CV  /  Bio  /  PhD Thesis  /  Google Scholar  /  GitHub  /  Twitter

News
Blog Posts
Recent Talk (June 2020)

IRIS Lab
Current PhD students
Frederik Ebert
Suraj Nair
Tianhe Yu
Allan Zhou
Annie Xie
Eric Mitchell
Evan Z. Liu

Former Visiting PhD students
Lisa Lee

Teaching

Stanford CS330: Deep Multi-Task and Meta Learning , Fall 2020 - Instructor
Stanford CS221: Artificial Intelligence: Principles and Techniques , Spring 2020 Co-Instructor
Stanford CS330: Deep Multi-Task and Meta Learning , Fall 2019 - Instructor
UCB CS294-112: Deep Reinforcement Learning , Spring 2017 - Co-Instructor
UCB CS188: Introduction to Artificial Intelligence , Spring 2015 - Graduate Student Instructor (GSI)
MIT 6.S080: Introduction to Inference , Spring 2014 - Teaching Assistant (TA)
MIT 6.141: Robotics: Science and Systems , Spring 2013 - Lab Assistant (LA)
MIT 6.02: Digital Communication Systems , Spring 2012 - Lab Assistant (LA)

Tutorials and Lectures
  • At ICML 2019 and CVPR 2019, I gave an invited tutorial on Meta-Learning: from Few-Shot Learning to Rapid Reinforcement Learning. Slides, video, and references are linked here.
  • In December 2018, I gave a tutorial on model-based reinforcement learning at the CIFAR LMB program meeting (slides here).
  • At ICML 2017, I gave a tutorial with Sergey Levine on Deep Reinforcement Learning, Decision Making, and Control (slides here, video here).
  • In August 2017, I gave guest lectures on model-based reinforcement learning and inverse reinforcement learning at the Deep RL Bootcamp (slides here and here, videos here and here).
  • In Spring 2017, I co-taught a course on deep reinforcement learning at UC Berkeley. All lecture video and slides are available here.
Invited Talks
Publications

Preprints

Meta-Learning Symmetries by Reparameterization
Allan Zhou, Tom Knowles, Chelsea Finn
arXiv

Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift
Marvin Zhang*, Henrik Marklund* Abhishek Gupta, Sergey Levine, Chelsea Finn
arXiv

Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning
Evan Z. Liu, Aditi Raghunathan, Percy Liang, Chelsea Finn
arXiv / webpage

Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning
Ryan Julian, Benjamin Swanson, Gaurav Sukhatme, Sergey Levine, Chelsea Finn, Karol Hausman,
arXiv

Gradient Surgery for Multi-Task Learning
Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn
arXiv / code

Continuous Meta-Learning without Tasks
James Harrison, Apoorva Sharma, Chelsea Finn, Marco Pavone
arXiv

SMiRL: Surprise Minimizing RL in Dynamic Environments
Glen Berseth, Daniel Geng, Coline Devin, Chelsea Finn, Dinesh Jayaraman, Sergey Levine
arXiv / videos

Unsupervised Meta-Learning for Reinforcement Learning
Abhishek Gupta, Ben Eysenbach, Chelsea Finn, Sergey Levine
arXiv

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
arXiv / project page

Goal-Aware Prediction: Learning to Model What Matters
Suraj Nair, Silvio Savarese, Chelsea Finn
International Conference on Machine Learning (ICML), 2020
arXiv / project page / code

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
arXiv / project page / code

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
arXiv / video

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
arXiv / project page

Time Reversal as Self-Supervision
Suraj Nair, Mohammad Babaeizadeh, Chelsea Finn, Sergey Levine, Vikash Kumar
International Conference on Robotics and Automation (ICRA), 2020
arXiv / project page


Meta-Learning without Memorization
Mingzhang Yin, George Tucker, Mingyuan Zhou, Sergey Levine, Chelsea Finn
International Conference on Learning Representations (ICLR), 2020 (Spotlight)
arXiv / talk / slides / code

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
arXiv / project page

Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation
Suraj Nair, Chelsea Finn
International Conference on Learning Representations (ICLR), 2020
arXiv / project page / code


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
arXiv / videos / code

Learning to Interactively Learn and Assist
Mark Woodward, Chelsea Finn, Karol Hausman
AAAI Conference on Artificial Intelligence, 2020 (Oral)
arXiv / project page and interactive game / video overview

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)
arXiv

Meta-Learning with Implicit Gradients
Aravind Rajeswaran*, Chelsea Finn*, Sham Kakade, Sergey Levine
Neural Information Processing Systems (NeurIPS), 2019
arXiv / project page


Guided Meta-Policy Search
Russell Mendonca, Abhishek Gupta, Rosen Kralev, Pieter Abbeel, Sergey Levine, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2019 (Spotlight)
arXiv / project page / code

Meta-Inverse Reinforcement Learning with Probabilistic Context Variables
Lantao Yu*, Tianhe Yu*, Chelsea Finn, Stefano Ermon
Neural Information Processing Systems (NeurIPS), 2019
arXiv

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
arXiv / project page




Online Meta-Learning
Chelsea Finn*, Aravind Rajeswaran*, Sham Kakade, Sergey Levine
International Conference on Machine Learning (ICML), 2019
arXiv


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
arXiv

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
arXiv / videos / blog post

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
arXiv / project page

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
arXiv / videos / code

Unsupervised Learning via Meta-Learning
Kyle Hsu, Sergey Levine, Chelsea Finn
International Conference on Learning Representations (ICLR), 2019
arXiv / project page / code

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
arXiv / project page

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
arXiv / project page

2018


Stochastic Adversarial Video Prediction
Alex Lee, Richard Zhang, Frederik Ebert, Pieter Abbeel, Chelsea Finn, Sergey Levine
arXiv / videos / code

Probabilistic Model-Agnostic Meta-Learning
Chelsea Finn*, Kelvin Xu*, Sergey Levine
Neural Information Processing Systems (NeurIPS), 2018
arXiv / supplementary website

Learning to Learn with Gradients
Chelsea Finn
PhD Dissertation, 2018

Few-Shot Goal Inference for Visuomotor Learning and Planning
Annie Xie, Avi Singh, Sergey Levine, Chelsea Finn
Conference on Learning (CoRL), 2018
arXiv / videos / code

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
arXiv / video

Universal Planning Networks
Aranvind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, Chelsea Finn
International Conference on Machine Learning (ICML), 2018
arXiv / videos / code

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
arXiv / video / code / blog post

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
arXiv

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
arXiv

Stochastic Variational Video Prediction
Mohammad Babaeizadeh, Chelsea Finn, Dumitru Erhan, Roy Campbell, Sergey Levine
International Conference on Learning Representations (ICLR), 2018
arXiv / code / video results

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
arXiv / project page / benchmark code

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)
Oral presentation at the NIPS 2017 Deep Reinforcement Learning Symposium
arXiv / code / result video / talk video

Self-Supervised Visual Planning with Temporal Skip Connections
Frederik Ebert, Chelsea Finn, Alex Lee, Sergey Levine
Conference on Robot Learning (CoRL), 2017 (Long Talk)
arXiv / code / video results and data

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Chelsea Finn, Pieter Abbeel, Sergey Levine
International Conference on Machine Learning (ICML), 2017
arXiv / blog post / code / video results

Generalizing Skills with Semi-Supervised Reinforcement Learning
Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine
International Conference on Learning Representations (ICLR), 2017
arXiv / video results / code

Deep Visual Foresight for Planning Robot Motion
Chelsea Finn, Sergey Levine
International Conference on Robotics and Automation (ICRA), 2017
Best Cognitive Robotics Paper Finalist
arXiv / video

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
arXiv / video / code

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
arXiv

Active One-Shot Learning
Mark Woodward, Chelsea Finn
NIPS Deep Reinforcement Learning Workshop, 2016 (Oral)
arXiv / video description / poster

Unsupervised Learning for Physical Interaction through Video Prediction
Chelsea Finn, Ian Goodfellow, Sergey Levine
Neural Information Processing Systems (NIPS), 2016
arXiv / videos / data / code

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
arXiv

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization
Chelsea Finn, Sergey Levine, Pieter Abbeel
International Conference on Machine Learning (ICML), 2016
Oral presentation at the NIPS 2016 Deep Learning Symposium
arXiv / video results / code / talk video

End-to-End Training of Deep Visuomotor Policies
Sergey Levine*, Chelsea Finn*, Trevor Darrell, Pieter Abbeel
CCC Blue Sky Ideas Award
Journal of Machine Learning Research (JMLR), 2016
arXiv / video / project page / code

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
arXiv / video

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
arXiv / video

2015

Bridging text spotting and SLAM with junction features.
Hsueh-Cheng Wang, Chelsea Finn, Liam Paull, Michael Kaess, Ruth Rosenholtz, Seth Teller, John Leonard
International Conference on Intelligent Robots and Systems (IROS), 2015:

Beyond Lowest-Warping Cost Action Selection in Trajectory Transfer
Dylan Hadfield-Menell, Alex X. Lee, Chelsea Finn, Eric Tzeng, Sandy Huang, Pieter Abbeel,
International Conference on Robotics and Automation (ICRA), 2015


This guy makes a nice webpage.