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 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 see this page.

CV  /  Bio  /  PhD Thesis  /  Google Scholar  /  Twitter  /  IRIS Lab

News
Blog Posts
Recent Talk (June 2022)

Students and Post-Docs

See this page for a list of lab members.

Teaching

Stanford CS330: Deep Multi-Task and Meta Learning - Fall 2019, Fall 2020, Fall 2021
Stanford CS221: Artificial Intelligence: Principles and Techniques - Spring 2020, Spring 2021
UCB CS294-112: Deep Reinforcement Learning Spring 2017

Tutorials and Lectures
  • In Fall 2019, I taught a new course on deep multi-task and meta learning. Lecture videos are available here.
  • 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
Selected Publications (See all)

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)
arXiv / code

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
arXiv / video

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
arXiv / project page

Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videos
Annie S. Chen, Suraj Nair, Chelsea Finn
Robotics: Science and Systems (RSS), 2021
arXiv / project page

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)
arXiv

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)
arXiv / webpage

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
arXiv / webpage / talk / blog post / code

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 (Oral)
arXiv / webpage

Meta-Learning Symmetries by Reparameterization
Allan Zhou, Tom Knowles, Chelsea Finn
International Conference on Learning Representations (ICLR), 2021
arXiv

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)
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

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

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
arXiv / project page, code, data / press

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

Language as an Abstraction for Hierarchical Reinforcement Learning
YiDing Jiang, Shixiang Gu, Kevin Murphy, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2019
arXiv / project page / code

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
arXiv / videos

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
arXiv / code

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

Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
Frederik Ebert*, Chelsea Finn*, Sudeep Dasari, Annie Xie, Alex Lee, Sergey Levine
arXiv / project page / code / data

Learning to Learn with Gradients
Chelsea Finn
PhD Dissertation, 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
arXiv / video / code / blog post

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

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

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

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

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

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


This guy makes a nice webpage.