Publications


2026

Q-Learning With World Models
Perry Dong, Yueru Jia, Chelsea Finn, Dorsa Sadigh
Pre-training Visual Dexterity in Simulation
Sarthak Kamat, Adam Rashid, Satvik Sharma, Aseem Doriwala, Chelsea Finn, Phillip Isola, C. Karen Liu
LLM-as-a-Verifier: A General-Purpose Verification Framework
Jacky Kwok, Shulu Li, Pranav Atreya, Yuejiang Liu, Yixing Jiang, Chelsea Finn, Marco Pavone, Ion Stoica, Azalia Mirhoseini
Freeform Preference Learning for Robotic Manipulation
Marcel Torne, Anubha Mahajan, Abhijnya Bhat, Chelsea Finn
Conference on Robot Learning (CoRL), 2026
SPIRAL: Learning to Search and Aggregate
Jubayer Ibn Hamid, Ifdita Hasan Orney, Michael Y. Li, Omar Shaikh, Yoonho Lee, Dorsa Sadigh, Chelsea Finn, Noah Goodman
Improving Robotic Generalist Policies via Flow Reversal Steering
Andy Tang, William Chen, Andrew Wagenmaker, Chelsea Finn, Sergey Levine
Conference on Robot Learning (CoRL), 2026
CHORUS: Decentralized Multi-Embodiment Collaboration with One VLA Policy
Ria Doshi, Tian Gao, Annie Chen, Chelsea Finn, Jeannette Bohg
Conference on Robot Learning (CoRL), 2026
DIRECT: When and Where Should You Allocate Test-Time Compute in Embodied Planners?
Jadelynn Dao, Milan Ganai, Yasmina Abukhadra, Ajay Sridhar, Mozhgan Nasr Azadani, Katie Luo, Clark Barrett, Jiajun Wu, Chelsea Finn, Marco Pavone
Conference on Robot Learning (CoRL), 2026
EXPO-FT: Sample-Efficient Reinforcement Learning Finetuning for Vision-Language-Action Models
Perry Dong, Kuo-Han Hung, Tian Gao, Dorsa Sadigh, Chelsea Finn
Conference on Robot Learning (CoRL), 2026
Long-Horizon Q-Learning: Accurate Value Learning via n-Step Inequalities
Armaan A. Abraham, Lucy Xiaoyang Shi, Chelsea Finn
Open-H-Embodiment: A Large-Scale Dataset for Enabling Foundation Models in Medical Robotics
Open-H-Embodiment Collaboration
FASTER: Value-Guided Sampling for Fast RL
Perry Dong, Alexander Swerdlow, Dorsa Sadigh, Chelsea Finn
Poly-EPO: Training Exploratory Reasoning Models
Ifdita Hasan Orney, Jubayer Ibn Hamid, S. S. Ramanujam, S. Wu, H. Hu, Noah Goodman, Dorsa Sadigh, Chelsea Finn
GIANTS: Generative Insight Anticipation from Scientific Literature
Joy He-Yueya, Anikait Singh, Ge Gao, Michael Y. Li, Sherry Yang, Chelsea Finn, Emma Brunskill, Noah D. Goodman
World Action Verifier: Self-Improving World Models via Forward-Inverse Asymmetry
Yuejiang Liu, Fan Feng, Lingjing Kong, Weifeng Lu, Jinzhou Tang, Kun Zhang, Kevin Murphy, Chelsea Finn, Yilun Du
Meta-Harness: End-to-End Optimization of Model Harnesses
Yoonho Lee, Roshen Nair, Qizheng Zhang, Kangwook Lee, Omar Khattab, Chelsea Finn
Conference on Language Modeling (COLM), 2026
Data Analogies Enable Efficient Cross-Embodiment Transfer
Jonathan Yang, Chelsea Finn, Dorsa Sadigh
MEM: Multi-Scale Embodied Memory for Vision Language Action Models
Marcel Torne, Karl Pertsch, Homer Walke, Kyle Vedder, Suraj Nair, Brian Ichter, Allen Z. Ren, Haohuan Wang, Jiaming Tang, Kyle Stachowicz, Karan Dhabalia, Michael Equi, Quan Vuong, Jost Tobias Springenberg, Sergey Levine, Chelsea Finn, Danny Driess
Conference on Robot Learning (CoRL), 2026
RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies
Y. Dai, H. Fu, J. Lee, Y. Liu, H. Zhang, Jonathan Yang, Chelsea Finn, Nima Fazeli, Joyce Chai
International Conference on Machine Learning (ICML), 2026
Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment
Jacky Kwok, Xilun Zhang, Mengdi Xu, Yuejiang Liu, Azalia Mirhoseini, Chelsea Finn, Marco Pavone
European Conference on Computer Vision (ECCV), 2026
VLAW: Iterative Co-Improvement of Vision-Language-Action Policy and World Model
Yanjiang Guo, Tony Lee, Lucy Xiaoyang Shi, Jianyu Chen, Percy Liang, Chelsea Finn
International Conference on Machine Learning (ICML), 2026
SteerVLA: Steering Vision-Language-Action Models in Long-Tail Driving Scenarios
T. Gao, C. Tan, Catherine Glossop, T. Gao, J. Sun, Kyle Stachowicz, S. Wu, Oier Mees, Chelsea Finn
TQL: Scaling Q-Functions with Transformers by Preventing Attention Collapse
Perry Dong, Kuo-Han Hung, Alexander Swerdlow, Dorsa Sadigh, Chelsea Finn
International Conference on Machine Learning (ICML), 2026
RoboReward: General-Purpose Vision-Language Reward Models for Robotics
Tony Lee, Andrew Wagenmaker, Karl Pertsch, Percy Liang, Sergey Levine, Chelsea Finn
Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data
J. W. Kim, K. Wang, Zipeng Fu, S. Chen, J. Lai, Chelsea Finn
Conference on Computer Vision and Pattern Recognition (CVPR), 2026
Posterior Behavioral Cloning: Pretraining BC Policies for Efficient RL Finetuning
Andrew Wagenmaker, Perry Dong, Raymond Tsao, Chelsea Finn, Sergey Levine
International Conference on Machine Learning (ICML), 2026
MemER: Scaling Up Memory for Robot Control via Experience Retrieval
Ajay Sridhar, Jennifer Pan, Satvik Sharma, Chelsea Finn
International Conference on Learning Representations (ICLR), 2026
Ctrl-World: A Controllable Generative World Model for Robot Manipulation
Yanjiang Guo, Lucy Xiaoyang Shi, Jianyu Chen, Chelsea Finn
International Conference on Learning Representations (ICLR), 2026
Value Flows
Perry Dong, Chongyi Zheng, Chelsea Finn, Dorsa Sadigh, Benjamin Eysenbach
International Conference on Learning Representations (ICLR), 2026
RLAD: Training LLMs to Discover Abstractions for Solving Reasoning Problems
Yuxiao Qu, Anikait Singh, Yoonho Lee, Amrith Setlur, Ruslan Salakhutdinov, Chelsea Finn, Aviral Kumar
International Conference on Learning Representations (ICLR), 2026
Polychromic Objectives for Reinforcement Learning
Jubayer Ibn Hamid, Ifdita Hasan Orney, Ellen Xu, Chelsea Finn, Dorsa Sadigh
International Conference on Learning Representations (ICLR), 2026
EXPO: Stable Reinforcement Learning with Expressive Policies
Perry Dong, Qiyang Li, Dorsa Sadigh, Chelsea Finn
International Conference on Learning Representations (ICLR), 2026
What Matters for Batch Online Reinforcement Learning in Robotics?
Perry Dong, Suvir Mirchandani, Dorsa Sadigh, Chelsea Finn
International Conference on Learning Representations (ICLR), 2026
FSPO: Few-Shot Optimization of Synthetic Preferences Personalizes to Real Users
Anikait Singh, Sheryl Hsu, Kyle Hsu, Eric Mitchell, Stefano Ermon, Tatsunori Hashimoto, Archit Sharma, Chelsea Finn
International Conference on Learning Representations (ICLR), 2026
PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies
Arhan Jain, Mingtong Zhang, Marcel Torne, Muhammad Zubair Irshad, Sergey Zakharov, Chelsea Finn

2025

Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment
Annie S Chen, Govind Chada, Laura Smith, Archit Sharma, Zipeng Fu, Sergey Levine, Chelsea Finn
Conference on Lifelong Learning Agents (CoLLAs), 2025
Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval
Sheryl Hsu, Omar Khattab, Chelsea Finn, Archit Sharma
International Conference on Learning Representations (ICLR), 2025
Invariance Co-training for Robot Visual Generalization
Jonathan Yang, Chelsea Finn, Dorsa Sadigh
Feedback Descent: Open-Ended Text Optimization via Pairwise Comparison
Yoonho Lee, Joseph Boen, Chelsea Finn
International Conference on Learning Representations (ICLR), 2025
Helpful DoggyBot: Open-World Object Fetching using Legged Robots and Vision-Language Models
Qi Wu, Zipeng Fu, Xuxin Cheng, Xiaolong Wang, Chelsea Finn
International Conference on Intelligent Robots and Systems (IROS), 2025
Affordance-Guided Reinforcement Learning via Visual Prompting
Olivia Y. Lee, Annie Xie, Kuan Fang, Karl Pertsch, Chelsea Finn
International Conference on Intelligent Robots and Systems (IROS), 2025
SRT-H: A Hierarchical Framework for Autonomous Surgery via Language-Conditioned Imitation Learning
Ji Woong Kim, Juo-Tung Chen, Pascal Hansen, Lucy X. Shi, Antony Goldenberg, Samuel Schmidgall, Paul Maria Scheikl, Anton Deguet, Brandon M. White, De Ru Tsai, Richard Cha, Jeffrey Jopling, Chelsea Finn, Axel Krieger
Science Robotics, 2025
RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies
Pranav Atreya, Karl Pertsch, Tony Lee, Moo Jin Kim, Arhan Jain, Artur Kuramshin, Clemens Eppner, Cyrus Neary, Edward Hu, Fabio Ramos, Jonathan Tremblay, Kanav Arora, Kirsty Ellis, Luca Macesanu, Marcel Torne Villasevil, Matthew Leonard, Meedeum Cho, Ozgur Aslan, Shivin Dass, Jie Wang, William Reger, Xingfang Yuan, Xuning Yang, Abhishek Gupta, Dinesh Jayaraman, Glen Berseth, Kostas Daniilidis, Roberto Martin-Martin, Youngwoon Lee, Percy Liang, Chelsea Finn, Sergey Levine
Conference on Robot Learning (CoRL), 2025
CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models
Qingqing Zhao, Yao Lu, Moo Jin Kim, Zipeng Fu, Zhuoyang Zhang, Yecheng Wu, Zhaoshuo Li, Qianli Ma, Song Han, Chelsea Finn, Ankur Handa, Ming-Yu Liu, Donglai Xiang, Gordon Wetzstein, Tsung-Yi Lin
Conference on Computer Vision and Pattern Recognition (CVPR), 2025
Reinforcement Learning via Implicit Imitation Guidance
Perry Dong, Alec M. Lessing, Annie S. Chen, Chelsea Finn
Just Enough Thinking: Efficient Reasoning with Adaptive Length Penalties Reinforcement Learning
Violet Xiang, Chase Blagden, Rafael Rafailov, Nathan Lile, Sang Truong, Chelsea Finn, Nick Haber
Neural Information Processing Systems (NeurIPS), 2025
SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning
David D. Yuan, Tony Z. Zhao, Kaylee Burns, Chelsea Finn
International Conference on Robotics and Automation (ICRA), 2025
RoboCrowd: Scaling Robot Data Collection through Crowdsourcing
Suvir Mirchandani, David D. Yuan, Kaylee Burns, Md Sazzad Islam, Tony Z. Zhao, Chelsea Finn, Dorsa Sadigh
International Conference on Robotics and Automation (ICRA), 2025
Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models
Annie S. Chen, Alec M. Lessing, Andy Tang, Govind Chada, Laura Smith, Sergey Levine, Chelsea Finn
International Conference on Robotics and Automation (ICRA), 2025
Learning Long-Context Diffusion Policies via Past-Token Prediction
Marcel Torne, Andy Tang, Yuejiang Liu, Chelsea Finn
Conference on Robot Learning (CoRL), 2025
Don't Cut Corners: Exact Conditions for Modularity in Biologically Inspired Representations
Will Dorrell, Kyle Hsu, Luke Hollingsworth, Jin Hwa Lee, Jiajun Wu, Chelsea Finn, Peter E. Latham, Timothy Edward John Behrens, James C. R. Whittington
International Conference on Learning Representations (ICLR), 2025
Bidirectional Decoding: Improving Action Chunking via Closed-Loop Resampling
Yuejiang Liu, Jubayer Ibn Hamid, Annie Xie, Yoonho Lee, Max Du, Chelsea Finn
International Conference on Learning Representations (ICLR), 2025
Latent Diffusion Planning for Imitation Learning
Amber Xie, Oleh Rybkin, Dorsa Sadigh, Chelsea Finn
International Conference on Machine Learning (ICML), 2025
Curating Demonstrations using Online Experience
Annie S. Chen, Alec M. Lessing, Yuejiang Liu, Chelsea Finn
Robotics: Science and Systems (RSS), 2025
Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success
Moo Jin Kim, Chelsea Finn, Percy Liang
Robotics: Science and Systems (RSS), 2025
Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models
Lucy Xiaoyang Shi, Brian Ichter, Michael Equi, Liyiming Ke, Karl Pertsch, Quan Vuong, James Tanner, Anna Walling, Haohuan Wang, Niccolo Fusai, Adrian Li-Bell, Danny Driess, Lachy Groom, Sergey Levine, Chelsea Finn
International Conference on Machine Learning (ICML), 2025
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought
Violet Xiang, Charlie Snell, Kanishk Gandhi, Alon Albalak, Anikait Singh, Chase Blagden, Duy Phung, Rafael Rafailov, Nathan Lile, Dakota Mahan, Louis Castricato, Jan-Philipp Franken, Nick Haber, Chelsea Finn
MJ-Video: Benchmarking and Rewarding Video Generation with Fine-Grained Video Preference
Haibo Tong, Zhaoyang Wang, Zhaorun Chen, Haonian Ji, Shi Qiu, Siwei Han, Kexin Geng, Zhongkai Xue, Yiyang Zhou, Peng Xia, Mingyu Ding, Rafael Rafailov, Chelsea Finn, Huaxiu Yao
Neural Information Processing Systems (NeurIPS), 2025 (Spotlight)
SutureBot: A Precision Framework & Benchmark For Autonomous End-to-End Suturing
Jesse Haworth, Juo-Tung Chen, Nigel Nelson, Ji Woong Kim, Masoud Moghani, Chelsea Finn, Axel Krieger
Neural Information Processing Systems (NeurIPS) Datasets & Benchmarks Track, 2025
Improving Test-Time Search for LLMs with Backtracking Against In-Context Value Verifiers
Anikait Singh, Kushal Arora, Sedrick Keh, Jean Mercat, Tatsunori Hashimoto, Chelsea Finn, Aviral Kumar
Workshop on Self-Improving Foundation Models Without Human Supervision at International Conference on Learning Representations (ICLR), 2025
PERSONA: A Reproducible Testbed for Pluralistic Alignment
Louis Castricato, Nathan Lile, Rafael Rafailov, Jan-Philipp Fränken, Chelsea Finn
International Conference on Computational Linguistics (COLING), 2025
MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation?
Zhaorun Chen, Yichao Du, Zichen Wen, Yiyang Zhou, Chenhang Cui, Zhenzhen Weng, Haoqin Tu, Chaoqi Wang, Zhengwei Tong, Qinglan Huang, Canyu Chen, Qinghao Ye, Zhihong Zhu, Yuqing Zhang, Jiawei Zhou, Zhuokai Zhao, Rafael Rafailov, Chelsea Finn, Huaxiu Yao
Neural Information Processing Systems (NeurIPS), 2025

2024

Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features
Annie S. Chen*, Yoonho Lee*, Amrith Setlur, Sergey Levine, Chelsea Finn
International Conference on Learning Representations (ICLR), 2024 (Spotlight)
Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning
Johnathan Wenjia Xie, Yoonho Lee, Annie S. Chen, Chelsea Finn
International Conference on Learning Representations (ICLR), 2024
AutoFT: Robust Fine-Tuning by Optimizing Hyperparameters on OOD Data
Caroline Choi*, Yoonho Lee*, Annie S. Chen, Allan Zhou, Aditi Raghunathan, Chelsea Finn
Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms
Rafael Rafailov, Yaswanth Chittepu, Ryan Park, Harshit Sikchi, Joey Hejna, W. Bradley Knox, Chelsea Finn, Scott Niekum
Neural Information Processing Systems (NeurIPS), 2024
A Critical Evaluation of AI Feedback for Aligning Large Language Models
Archit Sharma, Sedrick Keh, Eric Mitchell, Chelsea Finn, Kushal Arora, Thomas Kollar
Neural Information Processing Systems (NeurIPS), 2024
Universal Neural Functionals
Allan Zhou, Chelsea Finn, James Harrison
Neural Information Processing Systems (NeurIPS), 2024
Test-Time Alignment via Hypothesis Reweighting
Yoonho Lee, Jonathan Williams, Henrik Marklund, Archit Sharma, Eric Mitchell, Anikait Singh, Chelsea Finn
Transactions on Machine Learning Research (TMLR), 2024
Calibrating Language Models with Adaptive Temperature Scaling
Johnathan Xie, Annie S. Chen, Yoonho Lee, Eric Mitchell, Chelsea Finn
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2024
ALOHA Unleashed: A Simple Recipe for Robot Dexterity
Tony Z. Zhao, Jonathan Tompson, Danny Driess, Pete Florence, Kamyar Seyed Ghasemipour, Chelsea Finn, Ayzaan Wahid
Conference on Robot Learning (CoRL), 2024
Generative Reward Models
Dakota Mahan, Duy Van Phung, Rafael Rafailov, Chase Blagden, Nathan Lile, Louis Castricato, Jan-Philipp Fränken, Chelsea Finn, Alon Albalak
D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning
Rafael Rafailov, Kyle Hatch, Anikait Singh, Laura Smith, Aviral Kumar, Ilya Kostrikov, Philippe Hansen-Estruch, Victor Kolev, Philip Ball, Jiajun Wu, Chelsea Finn, Sergey Levine
Reinforcement Learning Conference (RLC), 2024
Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents
Pranav Putta, Edmund Mills, Naman Garg, Sumeet Motwani, Chelsea Finn, Divyansh Garg, Rafael Rafailov
Disentangling Length from Quality in Direct Preference Optimization
Ryan Park, Rafael Rafailov, Stefano Ermon, Chelsea Finn
Findings of the Association for Computational Linguistics (ACL), 2024
Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks
Ji Woong Kim, Tony Z. Zhao, Samuel Schmidgall, Anton Deguet, Marin Kobilarov, Chelsea Finn, Axel Krieger
Conference on Robot Learning (CoRL), 2024
Robotic Control via Embodied Chain-of-Thought Reasoning
Michał Zawalski, William Chen, Karl Pertsch, Oier Mees, Chelsea Finn, Sergey Levine
Conference on Robot Learning (CoRL), 2024
What Makes Pre-Trained Visual Representations Successful for Robust Manipulation?
Kaylee Burns, Zach Witzel, Jubayer Ibn Hamid, Tianhe Yu, Chelsea Finn, Karol Hausman
Conference on Robot Learning (CoRL), 2024
Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs
Hao-Tien Lewis Chiang, Zhuo Xu, Zipeng Fu, Mithun George Jacob, Tingnan Zhang, Tsang-Wei Edward Lee, Wenhao Yu, Connor Schenck, David Rendleman, Dhruv Shah, Fei Xia, Jasmine Hsu, Jonathan Hoech, Pete Florence, Sean Kirmani, Sumeet Singh, Vikas Sindhwani, Carolina Parada, Chelsea Finn, Peng Xu, Sergey Levine, Jie Tan
Conference on Robot Learning (CoRL), 2024
Learning to Explore in POMDPs with Informational Rewards
Annie Xie, Logan Mondal Bhamidipaty, Evan Zheran Liu, Joey Hong, Sergey Levine, Chelsea Finn
International Conference on Machine Learning (ICML), 2024
To Err is Robotic: Rapid Value-Based Trial-and-Error during Deployment
Maximilian Du, Alexander Khazatsky, Tobias Gerstenberg, Chelsea Finn
PIGEON: Predicting Image Geolocations
Lukas Haas, Michal Skreta, Silas Alberti, Chelsea Finn
Conference on Computer Vision and Pattern Recognition (CVPR), 2024
HumanPlus: Humanoid Shadowing and Imitation from Humans
Zipeng Fu, Qingqing Zhao, Qi Wu, Gordon Wetzstein, Chelsea Finn
Conference on Robot Learning (CoRL), 2024
OpenVLA: An Open-Source Vision-Language-Action Model
Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, Quan Vuong, Thomas Kollar, Benjamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, Chelsea Finn
Conference on Robot Learning (CoRL), 2024
Efficient Imitation Learning with Conservative World Models
Victor Kolev, Rafael Rafailov, Kyle Hatch, Jiajun Wu, Chelsea Finn
Learning for Dynamics and Control Conference (L4DC), 2024
Language Model Detectors Are Easily Optimized Against
Charlotte Nicks, Eric Mitchell, Rafael Rafailov, Archit Sharma, Christopher D. Manning, Chelsea Finn, Stefano Ermon
International Conference on Learning Representations (ICLR), 2024
Octo: An Open-Source Generalist Robot Policy
Octo Model Team, Dibya Ghosh, Homer Walke, Karl Pertsch, Kevin Black, Oier Mees, Sudeep Dasari, Joey Hejna, Tobias Kreiman, Charles Xu, Jianlan Luo, You Liang Tan, Lawrence Yunliang Chen, Pannag Sanketi, Quan Vuong, Ted Xiao, Dorsa Sadigh, Chelsea Finn, Sergey Levine
Robotics: Science and Systems (RSS), 2024
SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning
Jianlan Luo, Zheyuan Hu, Charles Xu, You Liang Tan, Jacob Berg, Archit Sharma, Stefan Schaal, Chelsea Finn, Abhishek Gupta, Sergey Levine
IEEE International Conference on Robotics and Automation (ICRA), 2024
Mobile ALOHA: Learning Bimanual Mobile Manipulation using Low-Cost Whole-Body Teleoperation
Zipeng Fu, Tony Z. Zhao, Chelsea Finn
Conference on Robot Learning (CoRL), 2024
From r to Q*: Your Language Model is Secretly a Q-Function
Rafael Rafailov, Joey Hejna, Ryan Park, Chelsea Finn
Conference on Language Modeling (COLM), 2024
Yell At Your Robot: Improving On-the-Fly from Language Corrections
Lucy Xiaoyang Shi, Zheyuan Hu, Tony Z. Zhao, Archit Sharma, Karl Pertsch, Jianlan Luo, Sergey Levine, Chelsea Finn
Robotics: Science and Systems (RSS), 2024
RLVF: Learning from Verbal Feedback without Overgeneralization
Moritz Pascal Stephan, Alexander Khazatsky, Eric Mitchell, Annie S. Chen, Sheryl Hsu, Archit Sharma, Chelsea Finn
International Conference on Machine Learning (ICML), 2024
Understanding Preference Fine-Tuning for Large Language Models
Anikait Singh, Fahim Tajwar, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar
International Conference on Machine Learning (ICML), 2024
Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning
Kyle Hsu, Jubayer Ibn Hamid, Kaylee Burns, Chelsea Finn, Jiajun Wu
International Conference on Machine Learning (ICML), 2024
Efficient Data Collection for Robotic Manipulation via Compositional Generalization
Jensen Gao, Annie Xie, Ted Xiao, Chelsea Finn, Dorsa Sadigh
Robotics: Science and Systems (RSS), 2024
Pushing the Limits of Cross-Embodiment Learning for Manipulation and Navigation
Jonathan Yang, Catherine Glossop, Arjun Bhorkar, Dhruv Shah, Quan Vuong, Chelsea Finn, Dorsa Sadigh, Sergey Levine
Robotics: Science and Systems (RSS), 2024
A Fast and Accurate Machine Learning Autograder for the Breakout Assignment
Evan Zheran Liu, David Yuan, Ahmed Ahmed, Elyse Cornwall, Juliette Woodrow, Kaylee Burns, Allen Nie, Emma Brunskill, Chris Piech, Chelsea Finn
ACM Special Interest Group on Computer Science Education (SIGCSE) Technical Symposium, 2024
DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky, Karl Pertsch, Suraj Nair, Ashwin Balakrishna, Sudeep Dasari, Siddharth Karamcheti, Soroush Nasiriany, Mohan Kumar Srirama, Lawrence Yunliang Chen, Kirsty Ellis, Peter David Fagan, Joey Hejna, Masha Itkina, Marion Lepert, Yecheng Jason Ma, Patrick Tree Miller, Jimmy Wu, Suneel Belkhale, Shivin Dass, Huy Ha, Arhan Jain, Abraham Lee, Youngwoon Lee, Marius Memmel, Sungjae Park, Ilija Radosavovic, Kaiyuan Wang, Albert Zhan, Kevin Black, Cheng Chi, Kyle Beltran Hatch, Shan Lin, Jingpei Lu, Jean Mercat, Abdul Rehman, Pannag R. Sanketi, Archit Sharma, Cody Simpson, Quan Vuong, Homer Rich Walke, Blake Wulfe, Ted Xiao, Jonathan Heewon Yang, Arefeh Yavary, Tony Z. Zhao, Christopher Agia, Rohan Baijal, Mateo Guaman Castro, Daphne Chen, Qiuyu Chen, Trinity Chung, Jaimyn Drake, Ethan Paul Foster, Jensen Gao, David Antonio Herrera, Minho Heo, Kyle Hsu, Jiaheng Hu, Donovon Jackson, Charlotte Le, Yunshuang Li, Roy Lin, Zehan Ma, Abhiram Maddukuri, Suvir Mirchandani, Daniel Morton, Tony Nguyen, Abigail O'Neill, Rosario Scalise, Derick Seale, Victor Son, Stephen Tian, Emi Tran, Andrew E. Wang, Yilin Wu, Annie Xie, Jingyun Yang, Patrick Yin, Yunchu Zhang, Osbert Bastani, Glen Berseth, Jeannette Bohg, Ken Goldberg, Abhinav Gupta, Abhishek Gupta, Dinesh Jayaraman, Joseph J. Lim, Jitendra Malik, Roberto Martín-Martín, Subramanian Ramamoorthy, Dorsa Sadigh, Shuran Song, Jiajun Wu, Michael C. Yip, Yuke Zhu, Thomas Kollar, Sergey Levine, Chelsea Finn
Robotics: Science and Systems (RSS), 2024
Evaluating Real-World Robot Manipulation Policies in Simulation
Xuanlin Li, Kyle Hsu, Jiayuan Gu, Oier Mees, Karl Pertsch, Homer Walke, Chuyuan Fu, Ishikaa Lunawat, Isabel Sieh, Sean Kirmani, Sergey Levine, Jiajun Wu, Chelsea Finn, Hao Su, Quan Vuong, Ted Xiao
Conference on Robot Learning (CoRL), 2024
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Open X-Embodiment Collaboration
International Conference on Robotics and Automation (ICRA), 2024

2023

Meta-Learning Online Adaptation of Language Models
Nathan Hu, Eric Mitchell, Christopher D. Manning, Chelsea Finn
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023
Just ask for calibration: Strategies for eliciting calibrated confidence scores from language models fine-tuned with human feedback
Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, Christopher D. Manning
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2023
Fine-Tuning Language Models for Factuality
Katherine Tian, Eric Mitchell, Huaxiu Yao, Christopher D. Manning, Chelsea Finn
International Conference on Learning Representations (ICLR), 2023
MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning
Rafael Rafailov, Kyle Beltran Hatch, Victor Kolev, John D Martin, Mariano Phielipp, Chelsea Finn
Conference on Robot Learning (CoRL), 2023
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
Yevgen Chebotar, Quan Vuong, Alex Irpan, Karol Hausman, Fei Xia, Yao Lu, Aviral Kumar, Tianhe Yu, Alexander Herzog, Karl Pertsch, Keerthana Gopalakrishnan, Julian Ibarz, Ofir Nachum, Sumedh Sontakke, Grecia Salazar, Huong T Tran, Jodilyn Peralta, Clayton Tan, Deeksha Manjunath, Jaspiar Singht, Brianna Zitkovich, Tomas Jackson, Kanishka Rao, Chelsea Finn, Sergey Levine
Conference on Robot Learning (CoRL), 2023
Robot parkour learning
Ziwen Zhuang*, Zipeng Fu*, Jianren Wang, Christopher Atkeson, Soeren Schwertfeger, Chelsea Finn, Hang Zhao
Conference on Robot Learning (CoRL), 2023
Bridgedata v2: A dataset for robot learning at scale
Homer Walke, Kevin Black, Abraham Lee, Moo Jin Kim, Max Du, Chongyi Zheng, Tony Zhao, Philippe Hansen-Estruch, Quan Vuong, Andre He, Vivek Myers, Kuan Fang, Chelsea Finn, Sergey Levine
Conference on Robot Learning (CoRL), 2023
Waypoint-based imitation learning for robotic manipulation
Lucy Xiaoyang Shi*, Archit Sharma*, Tony Z Zhao, Chelsea Finn
Conference on Robot Learning (CoRL), 2023
Polybot: Training One Policy Across Robots While Embracing Variability
Jonathan Yang, Dorsa Sadigh, Chelsea Finn
Conference on Robot Learning (CoRL), 2023
Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning
Archit Sharma, Ahmed M Ahmed, Rehaan Ahmad, Chelsea Finn
Conference on Robot Learning (CoRL), 2023
Open-world object manipulation using pre-trained vision-language models
Austin Stone, Ted Xiao, Yao Lu, Keerthana Gopalakrishnan, Kuang-Huei Lee, Quan Vuong, Paul Wohlhart, Brianna Zitkovich, Fei Xia, Chelsea Finn, Karol Hausman
Conference on Robot Learning (CoRL), 2023
Roboclip: one demonstration is enough to learn robot policies
Sumedh A Sontakke, Jesse Zhang, Sébastien MR Arnold, Karl Pertsch, Erdem Bıyık, Dorsa Sadigh, Chelsea Finn, Laurent Itti
Neural Information Processing Systems (NeurIPS), 2023
In-Context Decision-Making from Supervised Pretraining
Jonathan N Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill
Neural Information Processing Systems (NeurIPS), 2023
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov*, Archit Sharma*, Eric Mitchell*, Stefano Ermon, Christopher D Manning, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2023
Neural Functional Transformers
Allan Zhou, Kaien Yang, Yiding Jiang, Kaylee Burns, Winnie Xu, Samuel Sokota, J Zico Kolter, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2023
Cal-ql: Calibrated offline rl pre-training for efficient online fine-tuning
Mitsuhiko Nakamoto, Yuexiang Zhai, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, Sergey Levine
Neural Information Processing Systems (NeurIPS), 2023
Permutation Equivariant Neural Functionals
Allan Zhou, Kaien Yang, Kaylee Burns, Adriano Cardace, Yiding Jiang, Samuel Sokota, J. Zico Kolter, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2023
Disentanglement via Latent Quantization
Kyle Hsu, Will Dorrell, James C. R. Whittington, Jiajun Wu, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2023
Self-destructing models: Increasing the costs of harmful dual uses of foundation models
Peter Henderson, Eric Mitchell, Christopher Manning, Dan Jurafsky, Chelsea Finn
AAAI/ACM Conference on AI, Ethics, and Society, 2023
Learning fine-grained bimanual manipulation with low-cost hardware
Tony Z Zhao, Vikash Kumar, Sergey Levine, Chelsea Finn
Robotics: Science and Systems (RSS), 2023
Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets
Maximilian Du, Suraj Nair, Dorsa Sadigh, Chelsea Finn
Robotics: Science and Systems (RSS), 2023
Language-driven representation learning for robotics
Siddharth Karamcheti, Suraj Nair, Annie S Chen, Thomas Kollar, Chelsea Finn, Dorsa Sadigh, Percy Liang
Robotics: Science and Systems (RSS), 2023
Pre-training for robots: Offline rl enables learning new tasks from a handful of trials
Aviral Kumar, Anikait Singh, Frederik Ebert, Yanlai Yang, Chelsea Finn, Sergey Levine
Robotics: Science and Systems (RSS), 2023
Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning
Evan Zheran Liu, Sahaana Suri, Tong Mu, Allan Zhou, Chelsea Finn
International Conference on Machine Learning (ICML), 2023
DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature
Eric Mitchell, Yoonho Lee, Alexander Khazatsky, Christopher D. Manning, Chelsea Finn
International Conference on Machine Learning (ICML), 2023 (Oral)
Contrastive Example-Based Control
Kyle Beltran Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn
Learning for Dynamics and Control Conference (L4DC), 2023
NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis
Allan Zhou*, Moo Jin Kim*, Lirui Wang, Pete Florence, Chelsea Finn
Conference on Computer Vision and Pattern Recognition (CVPR), 2023
A Control-Centric Benchmark for Video Prediction
Stephen Tian, Chelsea Finn, Jiajun Wu
International Conference on Learning Representations (ICLR), 2023
Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts
Amrith Setlur, Don Dennis, Benjamin Eysenbach, Aditi Raghunathan, Chelsea Finn, Virginia Smith, Sergey Levine
International Conference on Learning Representations (ICLR), 2023
Surgical Fine-Tuning Improves Adaptation to Distribution Shifts
Yoonho Lee*, Annie S. Chen*, Fahim Tajwar, Ananya Kumar, Huaxiu Yao, Percy Liang, Chelsea Finn
International Conference on Learning Representations (ICLR), 2023
Diversify and disambiguate: Out-of-distribution robustness via disagreement
Yoonho Lee, Huaxiu Yao, Chelsea Finn
International Conference on Learning Representations (ICLR), 2023
Model-Based Adversarial Imitation Learning As Online Fine-Tuning
Rafael Rafailov, Victor Kolev, Kyle Beltran Hatch, John D Martin, Mariano Phielipp, Jiajun Wu, Chelsea Finn
Workshop on Reincarnating Reinforcement Learning at International Conference on Learning Representations (ICLR), 2023
Train Offline, Test Online: A Real Robot Learning Benchmark
Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama, Aravind Rajeswaran, Jyothish Pari, Kyle Hatch, Aryan Jain, Tianhe Yu, Pieter Abbeel, Lerrel Pinto, Chelsea Finn, Abhinav Gupta
International Conference on Robotics and Automation (ICRA), 2023
Clarify: Improving Model Robustness with Natural Language Corrections
Yoonho Lee, Michelle Lam, Helena Vasconcelos, Michael Bernstein, Chelsea Finn
ACM Symposium on User Interface Software and Technology (UIST), 2024
Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning
Jingyun Yang, Max Sobol Mark, Brandon Vu, Archit Sharma, Jeannette Bohg, Chelsea Finn
IEEE International Conference on Robotics and Automation (ICRA), 2023
Contrastive Preference Learning: Learning from Human Feedback without RL
Joey Hejna, Rafael Rafailov, Harshit Sikchi, Chelsea Finn, Scott Niekum, W. Bradley Knox, Dorsa Sadigh
International Conference on Learning Representations (ICLR), 2023
An Emulator for Fine-Tuning Large Language Models using Small Language Models
Eric Mitchell, Rafael Rafailov, Archit Sharma, Chelsea Finn, Christopher D. Manning
International Conference on Learning Representations (ICLR), 2023
Zero-shot robotic manipulation with pretrained image-editing diffusion models
Kevin Black, Mitsuhiko Nakamoto, Pranav Atreya, Homer Walke, Chelsea Finn, Aviral Kumar, Sergey Levine
International Conference on Learning Representations (ICLR), 2023
Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias
Max Sobol Mark, Archit Sharma, Fahim Tajwar, Rafael Rafailov, Sergey Levine, Chelsea Finn
Analyzing and mitigating object hallucination in large vision-language models
Yiyang Zhou, Chenhang Cui, Jaehong Yoon, Linjun Zhang, Zhun Deng, Chelsea Finn, Mohit Bansal, Huaxiu Yao
International Conference on Learning Representations (ICLR), 2023
Giving Robots a Hand: Learning Generalizable Manipulation with Eye-in-Hand Human Video Demonstrations
Moo Jin Kim, Jiajun Wu, Chelsea Finn
Decomposing the generalization gap in imitation learning for visual robotic manipulation
Annie Xie, Lisa Lee, Ted Xiao, Chelsea Finn
IEEE International Conference on Robotics and Automation (ICRA), 2023
Improving Domain Generalization with Domain Relations
Huaxiu Yao, Xinyu Yang, Xinyi Pan, Shengchao Liu, Pang Wei Koh, Chelsea Finn
International Conference on Learning Representations (ICLR), 2024
Conservative Prediction via Data-Driven Confidence Minimization
Caroline Choi*, Fahim Tajwar*, Yoonho Lee*, Huaxiu Yao, Ananya Kumar, Chelsea Finn
Transactions on Machine Learning Research (TMLR), 2023
A Tutorial on Meta-Reinforcement Learning
Jacob Beck, Risto Vuorio, Evan Zheran Liu, Zheng Xiong, Luisa Zintgraf, Chelsea Finn, Shimon Whiteson
Foundations and Trends in Machine Learning, 2023
Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts
Annie S Chen, Yoonho Lee, Amrith Setlur, Sergey Levine, Chelsea Finn

2022

Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference
Eric Mitchell, Joseph Noh, Siyan Li, Will Armstrong, Ananth Agarwal, Patrick Liu, Chelsea Finn, Christopher D. Manning
Conference on Empirical Methods in Natural Language Processing (EMNLP), 2022
MEMO: Test Time Robustness via Adaptation and Augmentation
Marvin Zhang, Sergey Levine, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2022
Bayesian Embeddings for Few-Shot Open World Recognition
John Willes, James Harrison, Ali Harakeh, Chelsea Finn, Marco Pavone, Steven Waslander
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2022
Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations
Huaxiu Yao*, Xinyu Yang*, Allan Zhou, Chelsea Finn
Transactions on Machine Learning Research (TMLR), 2022
Knowledge-Driven New Drug Recommendation
Zhenbang Wu, Huaxiu Yao, Zhe Su, David M Liebovitz, Lucas M Glass, James Zou, Chelsea Finn, Jimeng Sun
Giving Feedback on Interactive Student Programs with Meta-Exploration
Evan Zheran Liu, Moritz Stephan, Allen Nie, Chris Piech, Emma Brunskill, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2022 (Oral)
Learning Options via Compression
Yiding Jiang, Evan Zheran Liu, Benjamin Eysenbach, Zico Kolter, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2022
Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning
Xi Chen, Ali Ghadirzadeh, Tianhe Yu, Yuan Gao, Jianhao Wang, Wenzhe Li, Bin Liang, Chelsea Finn, Chongjie Zhang
Neural Information Processing Systems (NeurIPS), 2022
You Only Live Once: Single Life Reinforcement Learning
Annie S. Chen, Archit Sharma, Sergey Levine, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2022
C-Mixup: Improving Generalization in Regression
Huaxiu Yao*, Yiping Wang*, Linjun Zhang, James Zou, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2022
When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning
Annie Xie*, Fahim Tajwar*, Archit Sharma*, Chelsea Finn
Neural Information Processing Systems (NeurIPS), 2022
Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time
Huaxiu Yao*, Caroline Choi*, Bochuan Cao, Yoonho Lee, Pang Wei Koh, Chelsea Finn
Neural Information Processing Systems (NeurIPS) Datasets & Benchmarks Track, 2022
Contrastive Example-Based Control
Kyle Hatch, Sarthak Shetty, Ben Eysenbach, Tianhe Yu, Rafael Rafailov, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn
NeurIPS Deep Reinforcement Learning Workshop, 2022
Relaxing the Kolmogorov Structure Function for Realistic Computational Constraints
Yoonho Lee, Chelsea Finn, Stefano Ermon
InfoCog Workshop at Neural Information Processing Systems (NeurIPS), 2022
Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models
Ali Ghadirzadeh, Petra Poklukar, Karol Arndt, Chelsea Finn, Ville Kyrki, Danica Kragic, Marten Bjorkman
Journal of Machine Learning Research (JMLR), 2022
R3M: A Universal Visual Representation for Robot Manipulation
Suraj Nair, Aravind Rajeswaran, Vikash Kumar, Chelsea Finn, Abhinav Gupta
Conference on Robot Learning (CoRL), 2022
Offline Reinforcement Learning at Multiple Frequencies
Kaylee Burns, Tianhe Yu, Chelsea Finn, Karol Hausman
Conference on Robot Learning (CoRL), 2022
Lifelong Robotic Reinforcement Learning by Retaining Experiences
Annie Xie, Chelsea Finn
Conference on Lifelong Learning Agents (CoLLAs), 2022
Memory-Based Model Editing at Scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Christopher D. Manning, Chelsea Finn
International Conference on Machine Learning (ICML), 2022
Improving Out-of-Distribution Robustness via Selective Augmentation
Huaxiu Yao*, Yu Wang*, Sai Li, Linjun Zhang, Weixin Liang, James Zou, Chelsea Finn
International Conference on Machine Learning (ICML), 2022
How to Leverage Unlabeled Data in Offline Reinforcement Learning
Tianhe Yu*, Aviral Kumar*, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine
International Conference on Machine Learning (ICML), 2022
Robust Policy Learning over Multiple Uncertainty Sets
Annie Xie, Shagun Sodhani, Chelsea Finn, Joelle Pineau, Amy Zhang
International Conference on Machine Learning (ICML), 2022
A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning
Archit Sharma*, Rehaan Ahmad*, Chelsea Finn
International Conference on Machine Learning (ICML), 2022
Policy Architectures for Compositional Generalization in Control
Allan Zhou, Vikash Kumar, Chelsea Finn, Aravind Rajeswaran
Reinforcement Learning Conference (RLC), 2022
Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations
Michael Zhang, Nimit Sohoni, Hongyang Zhang, Chelsea Finn, Christopher Re
International Conference on Machine Learning (ICML), 2022
Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning
Maximilian Du*, Olivia Y. Lee*, Suraj Nair, Chelsea Finn
Robotics: Science and Systems (RSS), 2022
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
Robotics: Science and Systems (RSS), 2022
Vision-Based Manipulators Need to Also See from Their Hands
Kyle Hsu*, Moo Jin Kim*, Rafael Rafailov, Jiajun Wu, Chelsea Finn
International Conference on Learning Representations (ICLR), 2022 (Oral)
Fast Model Editing at Scale
Eric Mitchell, Charles Lin, Antoine Bosselut, Chelsea Finn, Christopher D. Manning
International Conference on Learning Representations (ICLR), 2021
Meta-Learning with Fewer Tasks through Task Interpolation
Huaxiu Yao, Linjun Zhang, Chelsea Finn
International Conference on Learning Representations (ICLR), 2022 (Oral)
Autonomous Reinforcement Learning: Formalism and Benchmarking
Archit Sharma*, Kelvin Xu*, Nikhil Sardana, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn
International Conference on Learning Representations (ICLR), 2022
Do Deep Networks Transfer Invariances Across Classes?
Allan Zhou*, Fahim Tajwar*, Alexander Robey, Tom Knowles, George J. Pappas, Hamed Hassani, Chelsea Finn
International Conference on Learning Representations (ICLR), 2022
Extending the WILDS Benchmark for Unsupervised Adaptation
Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang
International Conference on Learning Representations (ICLR), 2022 (Oral)
CoMPS: Continual Meta Policy Search
Glen Berseth, Zhiwei Zhang, Grace Zhang, Chelsea Finn, Sergey Levine
International Conference on Learning Representations (ICLR), 2022

2021

ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback
Mike Wu, Noah Goodman, Chris Piech, Chelsea Finn
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-Based Offline Reinforcement Learning without Rewards
Kyle Hatch*, Tianhe Yu*, Rafael Rafailov, Chelsea Finn
NeurIPS Offline Reinforcement Learning Workshop, 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)
Few-shot learning with weak supervision
Ali Ghadirzadeh, Petra Poklukar, Xi Chen, Huaxiu Yao, Hossein Azizpour, Marten Bjorkman, Chelsea Finn, Danica Kragic
ICLR workshop on Learning to Learn, 2021
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms
Ali Ghadirzadeh, Xi Chen, Petra Poklukar, Chelsea Finn, Marten Bjorkman, Danica Kragic
International Conference on Intelligent Robots and Systems (IROS), 2021

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 Robot 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 Robot 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