Vision-Based Biometrics for Conservation. - Sai Ravela; James Duyck; Chelsea Finn (MCPR, 2013)

Relevance Feedback in Biometric Retrieval of Animal Photographs. - Chelsea Finn; James Duyck; Andy Hutcheon; Pablo Vera; Joaquín Salas; Sai Ravela (MCPR, 2014)

Beyond lowest-warping cost action selection in trajectory transfer. - Dylan Hadfield-Menell; Alex X. Lee; Chelsea Finn; Eric Tzeng; Sandy H. Huang; Pieter Abbeel (ICRA, 2015)

Bridging text spotting and SLAM with junction features. - Hsueh-Cheng Wang; Chelsea Finn; Liam Paull; Michael Kaess; Ruth Rosenholtz; Seth J. Teller; John J. Leonard (IROS, 2015)

Learning Compact Convolutional Neural Networks with Nested Dropout. - Chelsea Finn; Lisa Anne Hendricks; Trevor Darrell (ICLR (Workshop), 2015)

Learning Visual Feature Spaces for Robotic Manipulation with Deep Spatial Autoencoders. - Chelsea Finn; Xin Yu Tan; Yan Duan; Trevor Darrell; Sergey Levine; Pieter Abbeel (CoRR, 2015)

Towards Adapting Deep Visuomotor Representations from Simulated to Real Environments. - Eric Tzeng; Coline Devin; Judy Hoffman; Chelsea Finn; Xingchao Peng; Sergey Levine; Kate Saenko; Trevor Darrell (CoRR, 2015)

Policy Learning with Continuous Memory States for Partially Observed Robotic Control. - Marvin Zhang; Sergey Levine; Zoe McCarthy; Chelsea Finn; Pieter Abbeel (CoRR, 2015)

Sloop: A pattern retrieval engine for individual animal identification. - James Duyck; Chelsea Finn; Andy Hutcheon; Pablo Vera; Joaquín Salas; Sai Ravela (Pattern Recognit., 2015)

Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization. - Chelsea Finn; Sergey Levine; Pieter Abbeel (ICML, 2016)

Deep spatial autoencoders for visuomotor learning. - Chelsea Finn; Xin Yu Tan; Yan Duan; Trevor Darrell; Sergey Levine; Pieter Abbeel (ICRA, 2016)

Learning deep neural network policies with continuous memory states. - Marvin Zhang; Zoe McCarthy; Chelsea Finn; Sergey Levine; Pieter Abbeel (ICRA, 2016)

Unsupervised Learning for Physical Interaction through Video Prediction. - Chelsea Finn; Ian J. Goodfellow; Sergey Levine (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 (WAFR, 2016)

A Connection between Generative Adversarial Networks, Inverse Reinforcement Learning, and Energy-Based Models. - Chelsea Finn; Paul F. Christiano; Pieter Abbeel; Sergey Levine (CoRR, 2016)

End-to-End Training of Deep Visuomotor Policies. - Sergey Levine; Chelsea Finn; Trevor Darrell; Pieter Abbeel (J. Mach. Learn. Res., 2016)

Self-Supervised Visual Planning with Temporal Skip Connections. - Frederik Ebert; Chelsea Finn; Alex X. Lee; Sergey Levine (CoRL, 2017)

One-Shot Visual Imitation Learning via Meta-Learning. - Chelsea Finn; Tianhe Yu; Tianhao Zhang 001; Pieter Abbeel; Sergey Levine (CoRL, 2017)

Generalizing Skills with Semi-Supervised Reinforcement Learning. - Chelsea Finn; Tianhe Yu; Justin Fu; Pieter Abbeel; Sergey Levine (ICLR (Poster), 2017)

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks. - Chelsea Finn; Pieter Abbeel; Sergey Levine (ICML, 2017)

Deep visual foresight for planning robot motion. - Chelsea Finn; Sergey Levine (ICRA, 2017)

Reset-free guided policy search: Efficient deep reinforcement learning with stochastic initial states. - William Montgomery; Anurag Ajay; Chelsea Finn; Pieter Abbeel; Sergey Levine (ICRA, 2017)

Active One-shot Learning. - Mark Woodward; Chelsea Finn (CoRR, 2017)

Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning. - Frederik Ebert; Sudeep Dasari; Alex X. Lee; Sergey Levine; Chelsea Finn (CoRL, 2018)

Few-Shot Goal Inference for Visuomotor Learning and Planning. - Annie Xie; Avi Singh; Sergey Levine; Chelsea Finn (CoRL, 2018)

Stochastic Variational Video Prediction. - Mohammad Babaeizadeh; Chelsea Finn; Dumitru Erhan; Roy H. Campbell; Sergey Levine (ICLR (Poster), 2018)

Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm. - Chelsea Finn; Sergey Levine (ICLR (Poster), 2018)

Recasting Gradient-Based Meta-Learning as Hierarchical Bayes. - Erin Grant; Chelsea Finn; Sergey Levine; Trevor Darrell; Thomas L. Griffiths 001 (ICLR (Poster), 2018)

One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning. - Tianhe Yu; Chelsea Finn; Annie Xie; Sudeep Dasari; Tianhao Zhang 001; Pieter Abbeel; Sergey Levine (ICLR (Workshop), 2018)

Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control. - Aravind Srinivas; Allan Jabri; Pieter Abbeel; Sergey Levine; Chelsea Finn (ICML, 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 (ICRA, 2018)

Probabilistic Model-Agnostic Meta-Learning. - Chelsea Finn; Kelvin Xu; Sergey Levine (NeurIPS, 2018)

Learning to Adapt: Meta-Learning for Model-Based Control. - Ignasi Clavera; Anusha Nagabandi; Ronald S. Fearing; Pieter Abbeel; Sergey Levine; Chelsea Finn (CoRR, 2018)

Universal Planning Networks. - Aravind Srinivas; Allan Jabri; Pieter Abbeel; Sergey Levine; Chelsea Finn (CoRR, 2018)

Stochastic Adversarial Video Prediction. - Alex X. Lee; Richard Zhang; Frederik Ebert; Pieter Abbeel; Chelsea Finn; Sergey Levine (CoRR, 2018)

Unsupervised Meta-Learning for Reinforcement Learning. - Abhishek Gupta 004; Benjamin Eysenbach; Chelsea Finn; Sergey Levine (CoRR, 2018)

Time Reversal as Self-Supervision. - Suraj Nair 003; Mohammad Babaeizadeh; Chelsea Finn; Sergey Levine; Vikash Kumar (CoRR, 2018)

One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks. - Tianhe Yu; Pieter Abbeel; Sergey Levine; Chelsea Finn (CoRR, 2018)

Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control. - Frederik Ebert; Chelsea Finn; Sudeep Dasari; Annie Xie; Alex X. Lee; Sergey Levine (CoRR, 2018)

Learning to Learn with Gradients. - Chelsea Finn (2018)

NoRML: No-Reward Meta Learning. - Yuxiang Yang 007; Ken Caluwaerts; Atil Iscen; Jie Tan 001; Chelsea Finn (AAMAS, 2019)

RoboNet: Large-Scale Multi-Robot Learning. - Sudeep Dasari; Frederik Ebert; Stephen Tian; Suraj Nair 003; Bernadette Bucher; Karl Schmeckpeper; Siddharth Singh; Sergey Levine; Chelsea Finn (CoRL, 2019)

Entity Abstraction in Visual Model-Based Reinforcement Learning. - Rishi Veerapaneni; John D. Co-Reyes; Michael Chang 003; Michael Janner; Chelsea Finn; Jiajun Wu 001; Joshua B. Tenenbaum; Sergey Levine (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; Chelsea Finn; Sergey Levine (CoRL, 2019)

Unsupervised Learning via Meta-Learning. - Kyle Hsu; Sergey Levine; Chelsea Finn (ICLR (Poster), 2019)

Reasoning About Physical Interactions with Object-Oriented Prediction and Planning. - Michael Janner; Sergey Levine; William T. Freeman; Joshua B. Tenenbaum; Chelsea Finn; Jiajun Wu 001 (ICLR (Poster), 2019)

Learning to Adapt in Dynamic, Real-World Environments through Meta-Reinforcement Learning. - Anusha Nagabandi; Ignasi Clavera; Simin Liu; Ronald S. Fearing; Pieter Abbeel; Sergey Levine; Chelsea Finn (ICLR (Poster), 2019)

Deep Online Learning Via Meta-Learning: Continual Adaptation for Model-Based RL. - Anusha Nagabandi; Chelsea Finn; Sergey Levine (ICLR (Poster), 2019)

Online Meta-Learning. - Chelsea Finn; Aravind Rajeswaran; Sham M. Kakade; Sergey Levine (ICML, 2019)

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables. - Kate Rakelly; Aurick Zhou; Chelsea Finn; Sergey Levine; Deirdre Quillen (ICML, 2019)

Learning a Prior over Intent via Meta-Inverse Reinforcement Learning. - Kelvin Xu; Ellis Ratner; Anca D. Dragan; Sergey Levine; Chelsea Finn (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 (ICRA, 2019)

One-Shot Composition of Vision-Based Skills from Demonstration. - Tianhe Yu; Pieter Abbeel; Sergey Levine; Chelsea Finn (IROS, 2019)

Unsupervised Curricula for Visual Meta-Reinforcement Learning. - Allan Jabri; Kyle Hsu; Abhishek Gupta 004; Ben Eysenbach; Sergey Levine; Chelsea Finn (NeurIPS, 2019)

Language as an Abstraction for Hierarchical Deep Reinforcement Learning. - Yiding Jiang; Shixiang Gu; Kevin Murphy; Chelsea Finn (NeurIPS, 2019)

Guided Meta-Policy Search. - Russell Mendonca; Abhishek Gupta 004; Rosen Kralev; Pieter Abbeel; Sergey Levine; Chelsea Finn (NeurIPS, 2019)

Meta-Learning with Implicit Gradients. - Aravind Rajeswaran; Chelsea Finn; Sham M. Kakade; Sergey Levine (NeurIPS, 2019)

Meta-Inverse Reinforcement Learning with Probabilistic Context Variables. - Lantao Yu; Tianhe Yu; Chelsea Finn; Stefano Ermon (NeurIPS, 2019)

End-To-End Robotic Reinforcement Learning without Reward Engineering. - Avi Singh; Larry Yang; Chelsea Finn; Sergey Levine (Robotics: Science and Systems, 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, 2019)

Unsupervised Visuomotor Control through Distributional Planning Networks. - Tianhe Yu; Gleb Shevchuk; Dorsa Sadigh; Chelsea Finn (Robotics: Science and Systems, 2019)

Model-Based Reinforcement Learning for Atari. - Lukasz Kaiser; Mohammad Babaeizadeh; Piotr Milos; Blazej Osinski; Roy H. Campbell; Konrad Czechowski; Dumitru Erhan; Chelsea Finn; Piotr Kozakowski; Sergey Levine; Ryan Sepassi; George Tucker; et al. (CoRR, 2019)

VideoFlow: A Flow-Based Generative Model for Video. - Manoj Kumar; Mohammad Babaeizadeh; Dumitru Erhan; Chelsea Finn; Sergey Levine; Laurent Dinh; Durk Kingma (CoRR, 2019)

End-to-End Robotic Reinforcement Learning without Reward Engineering. - Avi Singh; Larry Yang; Kristian Hartikainen; Chelsea Finn; Sergey Levine (CoRR, 2019)

Watch, Try, Learn: Meta-Learning from Demonstrations and Reward. - Allan Zhou; Eric Jang; Daniel Kappler; Alexander Herzog; Mohi Khansari; Paul Wohlhart; Yunfei Bai; Mrinal Kalakrishnan; Sergey Levine; Chelsea Finn (CoRR, 2019)

Training an Interactive Helper. - Mark Woodward; Chelsea Finn; Karol Hausman (CoRR, 2019)

SMiRL: Surprise Minimizing RL in Dynamic Environments. - Glen Berseth; Daniel Geng; Coline Devin; Chelsea Finn; Dinesh Jayaraman; Sergey Levine (CoRR, 2019)

Learning to Interactively Learn and Assist. - Mark Woodward; Chelsea Finn; Karol Hausman (AAAI, 2020)

Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning. - Ryan Julian; Benjamin Swanson; Gaurav S. Sukhatme; Sergey Levine; Chelsea Finn; Karol Hausman (CoRL, 2020)

Reinforcement Learning with Videos: Combining Offline Observations with Interaction. - Karl Schmeckpeper; Oleh Rybkin; Kostas Daniilidis; Sergey Levine; Chelsea Finn (CoRL, 2020)

Learning Latent Representations to Influence Multi-Agent Interaction. - Annie Xie; Dylan P. Losey; Ryan Tolsma; Chelsea Finn; Dorsa Sadigh (CoRL, 2020)

MELD: Meta-Reinforcement Learning from Images via Latent State Models. - Zihao Zhao; Anusha Nagabandi; Kate Rakelly; Chelsea Finn; Sergey Levine (CoRL, 2020)

Learning Predictive Models from Observation and Interaction. - Karl Schmeckpeper; Annie Xie; Oleh Rybkin; Stephen Tian; Kostas Daniilidis; Sergey Levine; Chelsea Finn (ECCV (20), 2020)

Model Based Reinforcement Learning for Atari. - Lukasz Kaiser; Mohammad Babaeizadeh; Piotr Milos; Blazej Osinski; Roy H. Campbell; Konrad Czechowski; Dumitru Erhan; Chelsea Finn; Piotr Kozakowski; Sergey Levine; Afroz Mohiuddin; Ryan Sepassi; et al. (ICLR, 2020)

VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation. - Manoj Kumar; Mohammad Babaeizadeh; Dumitru Erhan; Chelsea Finn; Sergey Levine; Laurent Dinh; Durk Kingma (ICLR, 2020)

Hierarchical Foresight: Self-Supervised Learning of Long-Horizon Tasks via Visual Subgoal Generation. - Suraj Nair 003; Chelsea Finn (ICLR, 2020)

Meta-Learning without Memorization. - Mingzhang Yin; George Tucker; Mingyuan Zhou; Sergey Levine; Chelsea Finn (ICLR, 2020)

Watch, Try, Learn: Meta-Learning from Demonstrations and Rewards. - Allan Zhou; Eric Jang; Daniel Kappler; Alexander Herzog; Mohi Khansari; Paul Wohlhart; Yunfei Bai; Mrinal Kalakrishnan; Sergey Levine; Chelsea Finn (ICLR, 2020)

On the Expressivity of Neural Networks for Deep Reinforcement Learning. - Kefan Dong; Yuping Luo; Tianhe Yu; Chelsea Finn; Tengyu Ma 001 (ICML, 2020)

Goal-Aware Prediction: Learning to Model What Matters. - Suraj Nair 003; Silvio Savarese; Chelsea Finn (ICML, 2020)

Cautious Adaptation For Reinforcement Learning in Safety-Critical Settings. - Jesse Zhang; Brian Cheung; Chelsea Finn; Sergey Levine; Dinesh Jayaraman (ICML, 2020)

TRASS: Time Reversal as Self-Supervision. - Suraj Nair 003; Mohammad Babaeizadeh; Chelsea Finn; Sergey Levine; Vikash Kumar (ICRA, 2020)

OmniTact: A Multi-Directional High-Resolution Touch Sensor. - Akhil Padmanabha; Frederik Ebert; Stephen Tian; Roberto Calandra; Chelsea Finn; Sergey Levine (ICRA, 2020)

Scalable Multi-Task Imitation Learning with Autonomous Improvement. - Avi Singh; Eric Jang; Alexander Irpan; Daniel Kappler; Murtaza Dalal; Sergey Levine; Mohi Khansari; Chelsea Finn (ICRA, 2020)

Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning. - Xingyou Song; Yuxiang Yang 007; Krzysztof Choromanski; Ken Caluwaerts; Wenbo Gao; Chelsea Finn; Jie Tan 001 (IROS, 2020)

Continuous Meta-Learning without Tasks. - James Harrison; Apoorva Sharma; Chelsea Finn; Marco Pavone 001 (NeurIPS, 2020)

One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL. - Saurabh Kumar 004; Aviral Kumar; Sergey Levine; Chelsea Finn (NeurIPS, 2020)

Weakly-Supervised Reinforcement Learning for Controllable Behavior. - Lisa Lee; Ben Eysenbach; Ruslan Salakhutdinov; Shixiang Shane Gu; Chelsea Finn (NeurIPS, 2020)

Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors. - Karl Pertsch; Oleh Rybkin; Frederik Ebert; Shenghao Zhou; Dinesh Jayaraman; Chelsea Finn; Sergey Levine (NeurIPS, 2020)

Continual Learning of Control Primitives : Skill Discovery via Reset-Games. - Kelvin Xu; Siddharth Verma; Chelsea Finn; Sergey Levine (NeurIPS, 2020)

Gradient Surgery for Multi-Task Learning. - Tianhe Yu; Saurabh Kumar 004; Abhishek Gupta 004; Sergey Levine; Karol Hausman; Chelsea Finn (NeurIPS, 2020)

MOPO: Model-based Offline Policy Optimization. - Tianhe Yu; Garrett Thomas; Lantao Yu; Stefano Ermon; James Y. Zou; Sergey Levine; Chelsea Finn; Tengyu Ma 001 (NeurIPS, 2020)

Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation. - Ryan Julian; Benjamin Swanson; Gaurav S. Sukhatme; Sergey Levine; Chelsea Finn; Karol Hausman (CoRR, 2020)

Meta-Reinforcement Learning Robust to Distributional Shift via Model Identification and Experience Relabeling. - Russell Mendonca; Xinyang Geng; Chelsea Finn; Sergey Levine (CoRR, 2020)

Deep Reinforcement Learning amidst Lifelong Non-Stationarity. - Annie Xie; James Harrison; Chelsea Finn (CoRR, 2020)

Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors. - Karl Pertsch; Oleh Rybkin; Frederik Ebert; Chelsea Finn; Dinesh Jayaraman; Sergey Levine (CoRR, 2020)

Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift. - Marvin Zhang; Henrik Marklund; Abhishek Gupta 004; Sergey Levine; Chelsea Finn (CoRR, 2020)

Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning. - Evan Zheran Liu; Aditi Raghunathan; Percy Liang; Chelsea Finn (CoRR, 2020)

MELD: Meta-Reinforcement Learning from Images via Latent State Models. - Tony Z. Zhao; Anusha Nagabandi; Kate Rakelly; Chelsea Finn; Sergey Levine (CoRR, 2020)

Learning to be Safe: Deep RL with a Safety Critic. - Krishnan Srinivasan; Benjamin Eysenbach; Sehoon Ha; Jie Tan 001; Chelsea Finn (CoRR, 2020)

Measuring and Harnessing Transference in Multi-Task Learning. - Christopher Fifty; Ehsan Amid; Zhe Zhao 001; Tianhe Yu; Rohan Anil; Chelsea Finn (CoRR, 2020)

Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement Learning. - Mohammad Babaeizadeh; Mohammad Taghi Saffar; Danijar Hafner; Harini Kannan; Chelsea Finn; Sergey Levine; Dumitru Erhan (CoRR, 2020)

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; et al. (CoRR, 2020)

Variable-Shot Adaptation for Online Meta-Learning. - Tianhe Yu; Xinyang Geng; Chelsea Finn; Sergey Levine (CoRR, 2020)

Challenges of Acquiring Compositional Inductive Biases via Meta-Learning. - Eric Mitchell; Chelsea Finn; Christopher D. Manning (MetaDL@AAAI, 2021)

BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning. - Eric Jang; Alex Irpan; Mohi Khansari; Daniel Kappler; Frederik Ebert; Corey Lynch; Sergey Levine; Chelsea Finn (CoRL, 2021)

Scaling Up Multi-Task Robotic Reinforcement Learning. - Dmitry Kalashnikov; Jake Varley; Yevgen Chebotar; Benjamin Swanson; Rico Jonschkowski; Chelsea Finn; Sergey Levine; Karol Hausman (CoRL, 2021)

A Workflow for Offline Model-Free Robotic Reinforcement Learning. - Aviral Kumar; Anikait Singh; Stephen Tian; Chelsea Finn; Sergey Levine (CoRL, 2021)

Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation. - Suraj Nair 003; Eric Mitchell; Kevin Chen 001; Brian Ichter; Silvio Savarese; Chelsea Finn (CoRL, 2021)

Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks. - Bohan Wu; Suraj Nair 003; Li Fei-Fei 001; Chelsea Finn (CoRL, 2021)

Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction. - Bohan Wu; Suraj Nair 003; Roberto Martín-Martín; Li Fei-Fei 001; Chelsea Finn (CVPR, 2021)

SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments. - Glen Berseth; Daniel Geng; Coline Manon Devin; Nicholas Rhinehart; Chelsea Finn; Dinesh Jayaraman; Sergey Levine (ICLR, 2021)

Model-Based Visual Planning with Self-Supervised Functional Distances. - Stephen Tian; Suraj Nair 003; Frederik Ebert; Sudeep Dasari; Benjamin Eysenbach; Chelsea Finn; Sergey Levine (ICLR, 2021)

Meta-learning Symmetries by Reparameterization. - Allan Zhou; Tom Knowles; Chelsea Finn (ICLR, 2021)

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills. - Yevgen Chebotar; Karol Hausman; Yao Lu 006; Ted Xiao; Dmitry Kalashnikov; Jacob Varley; Alex Irpan; Benjamin Eysenbach; Ryan Julian; Chelsea Finn; Sergey Levine (ICML, 2021)

Catformer: Designing Stable Transformers via Sensitivity Analysis. - Jared Quincy Davis; Albert Gu; Krzysztof Choromanski; Tri Dao; Christopher Ré; Chelsea Finn; Percy Liang (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; Irena Gao; Tony Lee; Etienne David; et al. (ICML, 2021)

Just Train Twice: Improving Group Robustness without Training Group Information. - Evan Zheran Liu; Behzad Haghgoo; Annie S. Chen; Aditi Raghunathan; Pang Wei Koh; Shiori Sagawa; Percy Liang; Chelsea Finn (ICML, 2021)

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices. - Evan Zheran Liu; Aditi Raghunathan; Percy Liang; Chelsea Finn (ICML, 2021)

Offline Meta-Reinforcement Learning with Advantage Weighting. - Eric Mitchell; Rafael Rafailov; Xue Bin Peng; Sergey Levine; Chelsea Finn (ICML, 2021)

Deep Reinforcement Learning amidst Continual Structured Non-Stationarity. - Annie Xie; James Harrison; Chelsea Finn (ICML, 2021)

Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms. - Ali Ghadirzadeh; Xi Chen 051; Petra Poklukar; Chelsea Finn; Mårten Björkman; Danica Kragic (IROS, 2021)

Offline Reinforcement Learning from Images with Latent Space Models. - Rafael Rafailov; Tianhe Yu; Aravind Rajeswaran; Chelsea Finn (L4DC, 2021)

Noether Networks: meta-learning useful conserved quantities. - Ferran Alet; Dylan Doblar; Allan Zhou; Josh Tenenbaum 001; Kenji Kawaguchi; Chelsea Finn (NeurIPS, 2021)

Efficiently Identifying Task Groupings for Multi-Task Learning. - Chris Fifty; Ehsan Amid; Zhe Zhao 001; Tianhe Yu; Rohan Anil; Chelsea Finn (NeurIPS, 2021)

Visual Adversarial Imitation Learning using Variational Models. - Rafael Rafailov; Tianhe Yu; Aravind Rajeswaran; Chelsea Finn (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 (NeurIPS, 2021)

Autonomous Reinforcement Learning via Subgoal Curricula. - Archit Sharma; Abhishek Gupta 004; Sergey Levine; Karol Hausman; Chelsea Finn (NeurIPS, 2021)

Meta-learning with an Adaptive Task Scheduler. - Huaxiu Yao; Yu Wang 170; Ying Wei 001; Peilin Zhao; Mehrdad Mahdavi; Defu Lian; Chelsea Finn (NeurIPS, 2021)

Conservative Data Sharing for Multi-Task Offline Reinforcement Learning. - Tianhe Yu; Aviral Kumar; Yevgen Chebotar; Karol Hausman; Sergey Levine; Chelsea Finn (NeurIPS, 2021)

COMBO: Conservative Offline Model-Based Policy Optimization. - Tianhe Yu; Aviral Kumar; Rafael Rafailov; Aravind Rajeswaran; Sergey Levine; Chelsea Finn (NeurIPS, 2021)

Differentiable Annealed Importance Sampling and the Perils of Gradient Noise. - Guodong Zhang 006; Kyle Hsu; Jianing Li; Chelsea Finn; Roger B. Grosse (NeurIPS, 2021)

Adaptive Risk Minimization: Learning to Adapt to Domain Shift. - Marvin Zhang; Henrik Marklund; Nikita Dhawan; Abhishek Gupta 004; Sergey Levine; Chelsea Finn (NeurIPS, 2021)

Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videos. - Annie S. Chen; Suraj Nair 003; Chelsea Finn (Robotics: Science and Systems, 2021)

How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned. - Julian Ibarz; Jie Tan 001; Chelsea Finn; Mrinal Kalakrishnan; Peter Pastor; Sergey Levine (CoRR, 2021)

Discriminator Augmented Model-Based Reinforcement Learning. - Behzad Haghgoo; Allan Zhou; Archit Sharma; Chelsea Finn (CoRR, 2021)

Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills. - Yevgen Chebotar; Karol Hausman; Yao Lu 006; Ted Xiao; Dmitry Kalashnikov; Jake Varley; Alex Irpan; Benjamin Eysenbach; Ryan Julian; Chelsea Finn; Sergey Levine (CoRR, 2021)

MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale. - Dmitry Kalashnikov; Jacob Varley; Yevgen Chebotar; Benjamin Swanson; Rico Jonschkowski; Chelsea Finn; Sergey Levine; Karol Hausman (CoRR, 2021)

FitVid: Overfitting in Pixel-Level Video Prediction. - Mohammad Babaeizadeh; Mohammad Taghi Saffar; Suraj Nair 003; Sergey Levine; Chelsea Finn; Dumitru Erhan (CoRR, 2021)

Persistent Reinforcement Learning via Subgoal Curricula. - Archit Sharma; Abhishek Gupta 004; Sergey Levine; Karol Hausman; Chelsea Finn (CoRR, 2021)

ProtoTransformer: A Meta-Learning Approach to Providing Student Feedback. - Mike Wu; Noah D. Goodman; Chris Piech; Chelsea Finn (CoRR, 2021)

On the Opportunities and Risks of Foundation Models. - Rishi Bommasani; Drew A. Hudson; Ehsan Adeli 001; Russ B. Altman; Simran Arora; Sydney von Arx; Michael S. Bernstein; Jeannette Bohg; Antoine Bosselut; Emma Brunskill; Erik Brynjolfsson; Shyamal Buch; et al. (CoRR, 2021)

MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance. - Michael Luo; Ashwin Balakrishna; Brijen Thananjeyan; Suraj Nair 003; Julian Ibarz; Jie Tan 001; Chelsea Finn; Ion Stoica; Ken Goldberg (CoRR, 2021)

How to train your robot with deep reinforcement learning: lessons we have learned. - Julian Ibarz; Jie Tan 001; Chelsea Finn; Mrinal Kalakrishnan; Peter Pastor; Sergey Levine (Int. J. Robotics Res., 2021)

Batch Exploration With Examples for Scalable Robotic Reinforcement Learning. - Annie S. Chen; HyunJi Nam; Suraj Nair 003; Chelsea Finn (IEEE Robotics Autom. Lett., 2021)

Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones. - Brijen Thananjeyan; Ashwin Balakrishna; Suraj Nair 003; Michael Luo; Krishnan Srinivasan; Minho Hwang; Joseph E. Gonzalez; Julian Ibarz; Chelsea Finn; Ken Goldberg (IEEE Robotics Autom. Lett., 2021)

Lifelong Robotic Reinforcement Learning by Retaining Experiences. - Annie Xie; Chelsea Finn (CoLLAs, 2022)

Offline Reinforcement Learning at Multiple Frequencies. - Kaylee Burns; Tianhe Yu; Chelsea Finn; Karol Hausman (CoRL, 2022)

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances. - Brian Ichter; Anthony Brohan; Yevgen Chebotar; Chelsea Finn; Karol Hausman; Alexander Herzog; Daniel Ho; Julian Ibarz; Alex Irpan; Eric Jang; Ryan Julian; Dmitry Kalashnikov; et al. (CoRL, 2022)

R3M: A Universal Visual Representation for Robot Manipulation. - Suraj Nair 003; Aravind Rajeswaran; Vikash Kumar; Chelsea Finn; Abhinav Gupta 001 (CoRL, 2022)

Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference. - Eric Mitchell; Joseph J. Noh; Siyan Li; William S. Armstrong; Ananth Agarwal; Patrick Liu; Chelsea Finn; Christopher D. Manning (EMNLP, 2022)

CoMPS: Continual Meta Policy Search. - Glen Berseth; Zhiwei Zhang; Grace Zhang; Chelsea Finn; Sergey Levine (ICLR, 2022)

Vision-Based Manipulators Need to Also See from Their Hands. - Kyle Hsu; Moo Jin Kim; Rafael Rafailov; Jiajun Wu 001; Chelsea Finn (ICLR, 2022)

Fast Model Editing at Scale. - Eric Mitchell; Charles Lin; Antoine Bosselut; Chelsea Finn; Christopher D. Manning (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; et al. (ICLR, 2022)

Autonomous Reinforcement Learning: Formalism and Benchmarking. - Archit Sharma; Kelvin Xu; Nikhil Sardana; Abhishek Gupta 004; Karol Hausman; Sergey Levine; Chelsea Finn (ICLR, 2022)

Meta-Learning with Fewer Tasks through Task Interpolation. - Huaxiu Yao; Linjun Zhang; Chelsea Finn (ICLR, 2022)

Do deep networks transfer invariances across classes? - Allan Zhou; Fahim Tajwar; Alexander Robey; Tom Knowles; George J. Pappas; Hamed Hassani; Chelsea Finn (ICLR, 2022)

Memory-Based Model Editing at Scale. - Eric Mitchell; Charles Lin; Antoine Bosselut; Christopher D. Manning; Chelsea Finn (ICML, 2022)

A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning. - Archit Sharma; Rehaan Ahmad; Chelsea Finn (ICML, 2022)

Robust Policy Learning over Multiple Uncertainty Sets. - Annie Xie; Shagun Sodhani; Chelsea Finn; Joelle Pineau; Amy Zhang 001 (ICML, 2022)

Improving Out-of-Distribution Robustness via Selective Augmentation. - Huaxiu Yao; Yu Wang 170; Sai Li 005; Linjun Zhang; Weixin Liang; James Zou 001; Chelsea Finn (ICML, 2022)

How to Leverage Unlabeled Data in Offline Reinforcement Learning. - Tianhe Yu; Aviral Kumar; Yevgen Chebotar; Karol Hausman; Chelsea Finn; Sergey Levine (ICML, 2022)

Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations. - Michael Zhang; Nimit Sharad Sohoni; Hongyang R. Zhang; Chelsea Finn; Christopher Ré (ICML, 2022)

LAPO: Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning. - Xi Chen; Ali Ghadirzadeh; Tianhe Yu; Jianhao Wang; Alex Yuan Gao; Wenzhe Li; Liang Bin; Chelsea Finn; Chongjie Zhang (NeurIPS, 2022)

You Only Live Once: Single-Life Reinforcement Learning. - Annie S. Chen; Archit Sharma; Sergey Levine; Chelsea Finn (NeurIPS, 2022)

Learning Options via Compression. - Yiding Jiang; Evan Zheran Liu; Benjamin Eysenbach; J. Zico Kolter; Chelsea Finn (NeurIPS, 2022)

Giving Feedback on Interactive Student Programs with Meta-Exploration. - Evan Zheran Liu; Moritz Stephan; Allen Nie; Chris Piech; Emma Brunskill; Chelsea Finn (NeurIPS, 2022)

When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning. - Annie Xie; Fahim Tajwar; Archit Sharma; Chelsea Finn (NeurIPS, 2022)

Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time. - Huaxiu Yao; Caroline Choi; Bochuan Cao; Yoonho Lee 001; Pang Wei Koh; Chelsea Finn (NeurIPS, 2022)

C-Mixup: Improving Generalization in Regression. - Huaxiu Yao; Yiping Wang 003; Linjun Zhang; James Y. Zou; Chelsea Finn (NeurIPS, 2022)

MEMO: Test Time Robustness via Adaptation and Augmentation. - Marvin Zhang; Sergey Levine; Chelsea Finn (NeurIPS, 2022)

Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning. - Maximilian Du; Olivia Y. Lee; Suraj Nair 003; Chelsea Finn (Robotics: Science and Systems, 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, 2022)

Fully Online Meta-Learning Without Task Boundaries. - Jathushan Rajasegaran; Chelsea Finn; Sergey Levine (CoRR, 2022)

Diversify and Disambiguate: Learning From Underspecified Data. - Yoonho Lee 001; Huaxiu Yao; Chelsea Finn (CoRR, 2022)

Latent-Variable Advantage-Weighted Policy Optimization for Offline RL. - Xi Chen 051; Ali Ghadirzadeh; Tianhe Yu; Yuan Gao 024; Jianhao Wang; Wenzhe Li; Bin Liang 001; Chelsea Finn; Chongjie Zhang (CoRR, 2022)

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances. - Michael Ahn; Anthony Brohan; Noah Brown; Yevgen Chebotar; Omar Cortes; Byron David; Chelsea Finn; Keerthana Gopalakrishnan; Karol Hausman; Alexander Herzog; Daniel Ho; Jasmine Hsu; et al. (CoRR, 2022)

Learning to Reason With Relational Abstractions. - Andrew Joohun Nam; Mengye Ren; Chelsea Finn; James L. McClelland (CoRR, 2022)

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 (CoRR, 2022)

Knowledge-Driven New Drug Recommendation. - Zhenbang Wu; Huaxiu Yao; Zhe Su; David M. Liebovitz; Lucas M. Glass; James Zou 001; Chelsea Finn; Jimeng Sun 001 (CoRR, 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; Mårten Björkman (J. Mach. Learn. Res., 2022)

Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models. - Peter Henderson 002; Eric Mitchell; Christopher D. Manning; Dan Jurafsky; Chelsea Finn (AIES, 2023)

Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions. - Yevgen Chebotar; Quan Vuong; Karol Hausman; Fei Xia 002; Yao Lu 006; Alex Irpan; Aviral Kumar; Tianhe Yu; Alexander Herzog; Karl Pertsch; Keerthana Gopalakrishnan; Julian Ibarz; et al. (CoRL, 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 (CoRL, 2023)

Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning. - Archit Sharma; Ahmed M. Ahmed 004; Rehaan Ahmad; Chelsea Finn (CoRL, 2023)

Waypoint-Based Imitation Learning for Robotic Manipulation. - Lucy Xiaoyang Shi; Archit Sharma; Tony Z. Zhao; Chelsea Finn (CoRL, 2023)

Open-World Object Manipulation using Pre-Trained Vision-Language Models. - Austin Stone; Ted Xiao; Yao Lu 006; Keerthana Gopalakrishnan; Kuang-Huei Lee; Quan Vuong; Paul Wohlhart; Sean Kirmani; Brianna Zitkovich; Fei Xia 002; Chelsea Finn; Karol Hausman (CoRL, 2023)

BridgeData V2: A Dataset for Robot Learning at Scale. - Homer Rich Walke; Kevin Black; Tony Z. Zhao; Quan Vuong; Chongyi Zheng; Philippe Hansen-Estruch; Andre Wang He; Vivek Myers; Moo Jin Kim; Max Du; Abraham Lee; Kuan Fang; et al. (CoRL, 2023)

Polybot: Training One Policy Across Robots While Embracing Variability. - Jonathan Heewon Yang; Dorsa Sadigh; Chelsea Finn (CoRL, 2023)

Robot Parkour Learning. - Ziwen Zhuang; Zipeng Fu; Jianren Wang; Christopher G. Atkeson; Sören Schwertfeger; Chelsea Finn; Hang Zhao (CoRL, 2023)

RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control. - Brianna Zitkovich; Tianhe Yu; Sichun Xu; Peng Xu 010; Ted Xiao; Fei Xia 002; Jialin Wu; Paul Wohlhart; Stefan Welker; Ayzaan Wahid; Quan Vuong; Vincent Vanhoucke; et al. (CoRL, 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 (CVPR, 2023)

Meta-Learning Online Adaptation of Language Models. - Nathan Hu; Eric Mitchell; Christopher D. Manning; Chelsea Finn (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 (EMNLP, 2023)

Surgical Fine-Tuning Improves Adaptation to Distribution Shifts. - Yoonho Lee 001; Annie S. Chen; Fahim Tajwar; Ananya Kumar; Huaxiu Yao; Percy Liang; Chelsea Finn (ICLR, 2023)

Diversify and Disambiguate: Out-of-Distribution Robustness via Disagreement. - Yoonho Lee 001; Huaxiu Yao; Chelsea Finn (ICLR, 2023)

Bitrate-Constrained DRO: Beyond Worst Case Robustness To Unknown Group Shifts. - Amrith Setlur; Don Kurian Dennis; Benjamin Eysenbach; Aditi Raghunathan; Chelsea Finn; Virginia Smith; Sergey Levine (ICLR, 2023)

A Control-Centric Benchmark for Video Prediction. - Stephen Tian; Chelsea Finn; Jiajun Wu 001 (ICLR, 2023)

Simple Embodied Language Learning as a Byproduct of Meta-Reinforcement Learning. - Evan Zheran Liu; Sahaana Suri; Tong Mu; Allan Zhou; Chelsea Finn (ICML, 2023)

DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature. - Eric Mitchell; Yoonho Lee 001; Alexander Khazatsky; Christopher D. Manning; Chelsea Finn (ICML, 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 001 (ICRA, 2023)

Contrastive Example-Based Control. - Kyle Beltran Hatch; Benjamin Eysenbach; Rafael Rafailov; Tianhe Yu; Ruslan Salakhutdinov; Sergey Levine; Chelsea Finn (L4DC, 2023)

Supervised Pretraining Can Learn In-Context Reinforcement Learning. - Jonathan Lee 002; Annie Xie; Aldo Pacchiano; Yash Chandak; Chelsea Finn; Ofir Nachum; Emma Brunskill (NeurIPS, 2023)

Disentanglement via Latent Quantization. - Kyle Hsu; William Dorrell; James C. R. Whittington; Jiajun Wu 001; Chelsea Finn (NeurIPS, 2023)

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning. - Mitsuhiko Nakamoto; Simon Zhai; Anikait Singh; Max Sobol Mark; Yi Ma 001; Chelsea Finn; Aviral Kumar; Sergey Levine (NeurIPS, 2023)

Direct Preference Optimization: Your Language Model is Secretly a Reward Model. - Rafael Rafailov; Archit Sharma; Eric Mitchell; Christopher D. Manning; Stefano Ermon; Chelsea Finn (NeurIPS, 2023)

RoboCLIP: One Demonstration is Enough to Learn Robot Policies. - Sumedh Sontakke; Jesse Zhang; Sébastien M. R. Arnold; Karl Pertsch; Erdem Biyik; Dorsa Sadigh; Chelsea Finn; Laurent Itti (NeurIPS, 2023)

Permutation Equivariant Neural Functionals. - Allan Zhou; Kaien Yang; Kaylee Burns; Adriano Cardace; Yiding Jiang; Samuel Sokota; J. Zico Kolter; Chelsea Finn (NeurIPS, 2023)

Neural Functional Transformers. - Allan Zhou; Kaien Yang; Yiding Jiang; Kaylee Burns; Winnie Xu; Samuel Sokota; J. Zico Kolter; Chelsea Finn (NeurIPS, 2023)

RT-1: Robotics Transformer for Real-World Control at Scale. - Anthony Brohan; Noah Brown; Justice Carbajal; Yevgen Chebotar; Joseph Dabis; Chelsea Finn; Keerthana Gopalakrishnan; Karol Hausman; Alexander Herzog; Jasmine Hsu; Julian Ibarz; Brian Ichter; et al. (Robotics: Science and Systems, 2023)

Behavior Retrieval: Few-Shot Imitation Learning by Querying Unlabeled Datasets. - Maximilian Du; Suraj Nair 003; Dorsa Sadigh; Chelsea Finn (Robotics: Science and Systems, 2023)

Language-Driven Representation Learning for Robotics. - Siddharth Karamcheti; Suraj Nair 003; Annie S. Chen; Thomas Kollar; Chelsea Finn; Dorsa Sadigh; Percy Liang (Robotics: Science and Systems, 2023)

Pre-Training for Robots: Offline RL Enables Learning New Tasks in a Handful of Trials. - Aviral Kumar; Anikait Singh; Frederik D. Ebert; Mitsuhiko Nakamoto; Yanlai Yang; Chelsea Finn; Sergey Levine (Robotics: Science and Systems, 2023)

Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware. - Tony Z. Zhao; Vikash Kumar; Sergey Levine; Chelsea Finn (Robotics: Science and Systems, 2023)

A Survey of Meta-Reinforcement Learning. - Jacob Beck; Risto Vuorio; Evan Zheran Liu; Zheng Xiong; Luisa M. Zintgraf; Chelsea Finn; Shimon Whiteson (CoRR, 2023)

Leveraging Domain Relations for Domain Generalization. - Huaxiu Yao; Xinyu Yang 002; Xinyi Pan; Shengchao Liu; Pang Wei Koh; Chelsea Finn (CoRR, 2023)

Project and Probe: Sample-Efficient Domain Adaptation by Interpolating Orthogonal Features. - Annie S. Chen; Yoonho Lee 001; Amrith Setlur; Sergey Levine; Chelsea Finn (CoRR, 2023)

Permutation Equivariant Neural Functionals. - Allan Zhou; Kaien Yang; Kaylee Burns; Yiding Jiang; Samuel Sokota; J. Zico Kolter; Chelsea Finn (CoRR, 2023)

Open-World Object Manipulation using Pre-trained Vision-Language Models. - Austin Stone; Ted Xiao; Yao Lu 006; Keerthana Gopalakrishnan; Kuang-Huei Lee; Quan Vuong; Paul Wohlhart; Brianna Zitkovich; Fei Xia 002; Chelsea Finn; Karol Hausman (CoRR, 2023)

Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning. - Mitsuhiko Nakamoto; Yuexiang Zhai; Anikait Singh; Max Sobol Mark; Yi Ma 001; Chelsea Finn; Aviral Kumar; Sergey Levine (CoRR, 2023)

Confidence-Based Model Selection: When to Take Shortcuts for Subpopulation Shifts. - Annie S. Chen; Yoonho Lee 001; Amrith Setlur; Sergey Levine; Chelsea Finn (CoRR, 2023)

Giving Robots a Hand: Learning Generalizable Manipulation with Eye-in-Hand Human Video Demonstrations. - Moo Jin Kim; Jiajun Wu 001; Chelsea Finn (CoRR, 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 (CoRR, 2023)

Open X-Embodiment: Robotic Learning Datasets and RT-X Models. - Open X.-Embodiment Collaboration; Abhishek Padalkar; Acorn Pooley; Ajinkya Jain; Alex Bewley; Alexander Herzog; Alex Irpan; Alexander Khazatsky; Anant Raj; Anikait Singh; Anthony Brohan; Antonin Raffin; et al. (CoRR, 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 (CoRR, 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 (CoRR, 2023)

Adapt On-the-Go: Behavior Modulation for Single-Life Robot Deployment. - Annie S. Chen; Govind Chada; Laura M. Smith; Archit Sharma; Zipeng Fu; Sergey Levine; Chelsea Finn (CoRR, 2023)

Multi-Domain Long-Tailed Learning by Augmenting Disentangled Representations. - Xinyu Yang 002; Huaxiu Yao; Allan Zhou; Chelsea Finn (Trans. Mach. Learn. Res., 2023)

Disentangling Length from Quality in Direct Preference Optimization. - Ryan Park; Rafael Rafailov; Stefano Ermon; Chelsea Finn (ACL (Findings), 2024)

What Makes Pre-Trained Visual Representations Successful for Robust Manipulation? - Kaylee Burns; Zach Witzel; Jubayer Ibn Hamid; Tianhe Yu; Chelsea Finn; Karol Hausman (CoRL, 2024)

Mobile ALOHA: Learning Bimanual Mobile Manipulation using Low-Cost Whole-Body Teleoperation. - Zipeng Fu; Tony Z. Zhao; Chelsea Finn (CoRL, 2024)

HumanPlus: Humanoid Shadowing and Imitation from Humans. - Zipeng Fu; Qingqing Zhao; Qi Wu; Gordon Wetzstein; Chelsea Finn (CoRL, 2024)

OpenVLA: An Open-Source Vision-Language-Action Model. - Moo Jin Kim; Karl Pertsch; Siddharth Karamcheti; Ted Xiao; Ashwin Balakrishna; Suraj Nair 003; Rafael Rafailov; Ethan Paul Foster; Pannag R. Sanketi; Quan Vuong; Thomas Kollar; Benjamin Burchfiel; et al. (CoRL, 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 (CoRL, 2024)

Evaluating Real-World Robot Manipulation Policies in Simulation. - Xuanlin Li; Kyle Hsu; Jiayuan Gu; Oier Mees; Karl Pertsch; Homer Rich Walke; Chuyuan Fu; Ishikaa Lunawat; Isabel Sieh; Sean Kirmani; Sergey Levine; Jiajun Wu 001; et al. (CoRL, 2024)

Mobility VLA: Multimodal Instruction Navigation with Long-Context VLMs and Topological Graphs. - Zhuo Xu; Hao-Tien Lewis Chiang; Zipeng Fu; Mithun George Jacob; Tingnan Zhang; Tsang-Wei Edward Lee; Wenhao Yu 003; Connor Schenck; David Rendleman; Dhruv Shah; Fei Xia 002; Jasmine Hsu; et al. (CoRL, 2024)

Robotic Control via Embodied Chain-of-Thought Reasoning. - Michal Zawalski; William Chen; Karl Pertsch; Oier Mees; Chelsea Finn; Sergey Levine (CoRL, 2024)

ALOHA Unleashed: A Simple Recipe for Robot Dexterity. - Tony Z. Zhao; Jonathan Tompson; Danny Driess; Pete Florence; Seyed Kamyar Seyed Ghasemipour; Chelsea Finn; Ayzaan Wahid (CoRL, 2024)

PIGEON: Predicting Image Geolocations. - Lukas Haas; Michal Skreta; Silas Alberti; Chelsea Finn (CVPR, 2024)

Calibrating Language Models with Adaptive Temperature Scaling. - Johnathan Xie; Annie S. Chen; Yoonho Lee 001; Eric Mitchell; Chelsea Finn (EMNLP, 2024)

Zero-Shot Robotic Manipulation with Pre-Trained Image-Editing Diffusion Models. - Kevin Black; Mitsuhiko Nakamoto; Pranav Atreya; Homer Rich Walke; Chelsea Finn; Aviral Kumar; Sergey Levine (ICLR, 2024)

Project and Probe: Sample-Efficient Adaptation by Interpolating Orthogonal Features. - Annie S. Chen; Yoonho Lee 001; Amrith Setlur; Sergey Levine; Chelsea Finn (ICLR, 2024)

RT-Trajectory: Robotic Task Generalization via Hindsight Trajectory Sketches. - Jiayuan Gu; Sean Kirmani; Paul Wohlhart; Yao Lu 006; Montserrat Gonzalez Arenas; Kanishka Rao; Wenhao Yu 003; Chuyuan Fu; Keerthana Gopalakrishnan; Zhuo Xu; Priya Sundaresan; Peng Xu 010; et al. (ICLR, 2024)

Contrastive Preference Learning: Learning from Human Feedback without Reinforcement Learning. - Joey Hejna; Rafael Rafailov; Harshit Sikchi; Chelsea Finn; Scott Niekum; W. Bradley Knox; Dorsa Sadigh (ICLR, 2024)

An Emulator for Fine-tuning Large Language Models using Small Language Models. - Eric Mitchell; Rafael Rafailov; Archit Sharma; Chelsea Finn; Christopher D. Manning (ICLR, 2024)

Language Model Detectors Are Easily Optimized Against. - Charlotte Nicks; Eric Mitchell; Rafael Rafailov; Archit Sharma; Christopher D. Manning; Chelsea Finn; Stefano Ermon (ICLR, 2024)

Fine-Tuning Language Models for Factuality. - Katherine Tian; Eric Mitchell; Huaxiu Yao; Christopher D. Manning; Chelsea Finn (ICLR, 2024)

Self-Guided Masked Autoencoders for Domain-Agnostic Self-Supervised Learning. - Johnathan Xie; Yoonho Lee 001; Annie S. Chen; Chelsea Finn (ICLR, 2024)

Improving Domain Generalization with Domain Relations. - Huaxiu Yao; Xinyu Yang 002; Xinyi Pan; Shengchao Liu; Pang Wei Koh; Chelsea Finn (ICLR, 2024)

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 (ICLR, 2024)

Tripod: Three Complementary Inductive Biases for Disentangled Representation Learning. - Kyle Hsu; Jubayer Ibn Hamid; Kaylee Burns; Chelsea Finn; Jiajun Wu 001 (ICML, 2024)

PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs. - Soroush Nasiriany; Fei Xia 002; Wenhao Yu 003; Ted Xiao; Jacky Liang; Ishita Dasgupta 001; Annie Xie; Danny Driess; Ayzaan Wahid; Zhuo Xu; Quan Vuong; Tingnan Zhang; et al. (ICML, 2024)

RLVF: Learning from Verbal Feedback without Overgeneralization. - Moritz Stephan; Alexander Khazatsky; Eric Mitchell; Annie S. Chen; Sheryl Hsu; Archit Sharma; Chelsea Finn (ICML, 2024)

Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. - Fahim Tajwar; Anikait Singh; Archit Sharma; Rafael Rafailov; Jeff Schneider 001; Tengyang Xie; Stefano Ermon; Chelsea Finn; Aviral Kumar (ICML, 2024)

Learning to Explore in POMDPs with Informational Rewards. - Annie Xie; Logan M. Bhamidipaty; Evan Zheran Liu; Joey Hong; Sergey Levine; Chelsea Finn (ICML, 2024)

SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning. - Jianlan Luo; Zheyuan Hu 003; Charles Xu 003; You Liang Tan; Jacob Berg; Archit Sharma; Stefan Schaal; Chelsea Finn; Abhishek Gupta 004; Sergey Levine (ICRA, 2024)

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration. - Abby O'Neill; Abdul Rehman; Abhiram Maddukuri; Abhishek Gupta 004; Abhishek Padalkar; Abraham Lee; Acorn Pooley; Agrim Gupta; Ajay Mandlekar; Ajinkya Jain; Albert Tung; Alex Bewley; et al. (ICRA, 2024)

Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation. - Annie Xie; Lisa Lee; Ted Xiao; Chelsea Finn (ICRA, 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 (ICRA, 2024)

Efficient imitation learning with conservative world models. - Victor Kolev; Rafael Rafailov; Kyle Hatch; Jiajun Wu 001; Chelsea Finn (L4DC, 2024)

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 (NeurIPS, 2024)

A Critical Evaluation of AI Feedback for Aligning Large Language Models. - Archit Sharma; Sedrick Scott Keh; Eric Mitchell; Chelsea Finn; Kushal Arora; Thomas Kollar (NeurIPS, 2024)

Universal Neural Functionals. - Allan Zhou; Chelsea Finn; James Harrison (NeurIPS, 2024)

D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning. - Rafael Rafailov; Kyle Beltran Hatch; Anikait Singh; Aviral Kumar; Laura M. Smith; Ilya Kostrikov; Philippe Hansen-Estruch; Victor Kolev; Philip J. Ball; Jiajun Wu 001; Sergey Levine; Chelsea Finn (RLJ, 2024)

Policy Architectures for Compositional Generalization in Control. - Allan Zhou; Vikash Kumar; Chelsea Finn; Aravind Rajeswaran (RLJ, 2024)

Efficient Data Collection for Robotic Manipulation via Compositional Generalization. - Jensen Gao; Annie Xie; Ted Xiao; Chelsea Finn; Dorsa Sadigh (Robotics: Science and Systems, 2024)

Octo: An Open-Source Generalist Robot Policy. - Dibya Ghosh; Homer Rich Walke; Karl Pertsch; Kevin Black; Oier Mees; Sudeep Dasari; Joey Hejna; Tobias Kreiman; Charles Xu 003; Jianlan Luo; You Liang Tan; Lawrence Yunliang Chen; et al. (Robotics: Science and Systems, 2024)

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset. - Alexander Khazatsky; Karl Pertsch; Suraj Nair 003; Ashwin Balakrishna; Sudeep Dasari; Siddharth Karamcheti; Soroush Nasiriany; Mohan Kumar Srirama; Lawrence Yunliang Chen; Kirsty Ellis; Peter David Fagan; Joey Hejna; et al. (Robotics: Science and Systems, 2024)

Yell At Your Robot: Improving On-the-Fly from Language Corrections. - Lucy Xiaoyang Shi; Zheyuan Hu 003; Tony Z. Zhao; Archit Sharma; Karl Pertsch; Jianlan Luo; Sergey Levine; Chelsea Finn (Robotics: Science and Systems, 2024)

Pushing the Limits of Cross-Embodiment Learning for Manipulation and Navigation. - Jonathan Heewon Yang; Catherine Glossop; Arjun Bhorkar; Dhruv Shah; Quan Vuong; Chelsea Finn; Dorsa Sadigh; Sergey Levine (Robotics: Science and Systems, 2024)

A Fast and Accurate Machine Learning Autograder for the Breakout Assignment. - Evan Zheran Liu; David Yuan 001; Ahmed Ahmed; Elyse Cornwall; Juliette Woodrow; Kaylee Burns; Allen Nie; Emma Brunskill; Chris Piech; Chelsea Finn (SIGCSE (1), 2024)

Clarify: Improving Model Robustness With Natural Language Corrections. - Yoonho Lee 001; Michelle S. Lam; Helena Vasconcelos; Michael S. Bernstein; Chelsea Finn (UIST, 2024)

Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation. - Zipeng Fu; Tony Z. Zhao; Chelsea Finn (CoRR, 2024)

AutoFT: Robust Fine-Tuning by Optimizing Hyperparameters on OOD Data. - Caroline Choi; Yoonho Lee 001; Annie S. Chen; Allan Zhou; Aditi Raghunathan; Chelsea Finn (CoRR, 2024)

AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents. - Michael Ahn; Debidatta Dwibedi; Chelsea Finn; Montse Gonzalez Arenas; Keerthana Gopalakrishnan; Karol Hausman; Brian Ichter; Alex Irpan; Nikhil J. Joshi; Ryan Julian; Sean Kirmani; Isabel Leal; et al. (CoRR, 2024)

Aligning Modalities in Vision Large Language Models via Preference Fine-tuning. - Yiyang Zhou; Chenhang Cui; Rafael Rafailov; Chelsea Finn; Huaxiu Yao (CoRR, 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 (CoRR, 2024)

From r to Q*: Your Language Model is Secretly a Q-Function. - Rafael Rafailov; Joey Hejna; Ryan Park; Chelsea Finn (CoRR, 2024)

ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation. - ALOHA 2 Team; Jorge Aldaco; Travis Armstrong; Robert Baruch; Jeff Bingham; Sanky Chan; Kenneth Draper; Debidatta Dwibedi; Chelsea Finn; Pete Florence; Spencer Goodrich; Wayne Gramlich; et al. (CoRR, 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 003; Jianlan Luo; You Liang Tan; et al. (CoRR, 2024)

OpenVLA: An Open-Source Vision-Language-Action Model. - Moo Jin Kim; Karl Pertsch; Siddharth Karamcheti; Ted Xiao; Ashwin Balakrishna; Suraj Nair 003; Rafael Rafailov; Ethan Paul Foster; Grace Lam; Pannag Sanketi; Quan Vuong; Thomas Kollar; et al. (CoRR, 2024)

To Err is Robotic: Rapid Value-Based Trial-and-Error during Deployment. - Maximilian Du; Alexander Khazatsky; Tobias Gerstenberg; Chelsea Finn (CoRR, 2024)

Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models. - Annie S. Chen; Alec M. Lessing; Andy Tang; Govind Chada; Laura M. Smith; Sergey Levine; Chelsea Finn (CoRR, 2024)

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; et al. (CoRR, 2024)

Affordance-Guided Reinforcement Learning via Visual Prompting. - Olivia Y. Lee; Annie Xie; Kuan Fang; Karl Pertsch; Chelsea Finn (CoRR, 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 (CoRR, 2024)

Bidirectional Decoding: Improving Action Chunking via Closed-Loop Resampling. - Yuejiang Liu; Jubayer Ibn Hamid; Annie Xie; Yoonho Lee 001; Maximilian Du; Chelsea Finn (CoRR, 2024)

Helpful DoggyBot: Open-World Object Fetching using Legged Robots and Vision-Language Models. - Qi Wu; Zipeng Fu; Xuxin Cheng; Xiaolong Wang 004; Chelsea Finn (CoRR, 2024)

Don't Cut Corners: Exact Conditions for Modularity in Biologically Inspired Representations. - Will Dorrell; Kyle Hsu; Luke Hollingsworth; Jin Hwa Lee; Jiajun Wu 001; Chelsea Finn; Peter E. Latham; Timothy Edward John Behrens; James C. R. Whittington (CoRR, 2024)

Generative Reward Models. - Dakota Mahan; Duy Phung; Rafael Rafailov; Chase Blagden; Nathan Lile; Louis Castricato; Jan-Philipp Fränken; Chelsea Finn; Alon Albalak (CoRR, 2024)

π0: A Vision-Language-Action Flow Model for General Robot Control. - Kevin Black; Noah Brown; Danny Driess; Adnan Esmail; Michael Equi; Chelsea Finn; Niccolo Fusai; Lachy Groom; Karol Hausman; Brian Ichter; Szymon Jakubczak; Tim Jones; et al. (CoRR, 2024)

RoboCrowd: Scaling Robot Data Collection through Crowdsourcing. - Suvir Mirchandani; David D. Yuan; Kaylee Burns; Md Sazzad Islam; Tony Z. Zhao; Chelsea Finn; Dorsa Sadigh (CoRR, 2024)

Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone. - Max Sobol Mark; Tian Gao; Georgia Gabriela Sampaio; Mohan Kumar Srirama; Archit Sharma; Chelsea Finn; Aviral Kumar (CoRR, 2024)

Test-Time Alignment via Hypothesis Reweighting. - Yoonho Lee 001; Jonathan Williams; Henrik Marklund; Archit Sharma; Eric Mitchell; Anikait Singh; Chelsea Finn (CoRR, 2024)

Conservative Prediction via Data-Driven Confidence Minimization. - Caroline Choi; Fahim Tajwar; Yoonho Lee 001; Huaxiu Yao; Ananya Kumar; Chelsea Finn (Trans. Mach. Learn. Res., 2024)

Bayesian Embeddings for Few-Shot Open World Recognition. - John Willes; James Harrison; Ali Harakeh; Chelsea Finn; Marco Pavone 001; Steven L. Waslander (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

PERSONA: A Reproducible Testbed for Pluralistic Alignment. - Louis Castricato; Nathan Lile; Rafael Rafailov; Jan-Philipp Fränken; Chelsea Finn (COLING, 2025)

CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models. - Qingqing Zhao; Yao Lu 006; Moo Jin Kim; Zipeng Fu; Zhuoyang Zhang; Yecheng Wu; Zhaoshuo Li; Qianli Ma; Song Han 003; Chelsea Finn; Ankur Handa; Tsung-Yi Lin; et al. (CVPR, 2025)

Range, not Independence, Drives Modularity in Biologically Inspired Representations. - Will Dorrell; Kyle Hsu; Luke Hollingsworth; Jin Hwa Lee; Jiajun Wu 001; Chelsea Finn; Peter E. Latham; Timothy Edward John Behrens; James C. R. Whittington (ICLR, 2025)

Grounding by Trying: LLMs with Reinforcement Learning-Enhanced Retrieval. - Sheryl Hsu; Omar Khattab; Chelsea Finn; Archit Sharma (ICLR, 2025)

Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling. - Yuejiang Liu; Jubayer Ibn Hamid; Annie Xie; Yoonho Lee 001; Max Du; Chelsea Finn (ICLR, 2025)

A Tutorial on Meta-Reinforcement Learning. - Jacob Beck; Risto Vuorio; Evan Zheran Liu; Zheng Xiong; Luisa M. Zintgraf; Chelsea Finn; Shimon Whiteson (Found. Trends Mach. Learn., 2025)

SRT-H: A hierarchical framework for autonomous surgery via language-conditioned imitation learning. - Ji Woong Kim; Juo-Tung Chen; Pascal Hansen; Lucy Xiaoyang Shi; Antony Goldenberg; Samuel Schmidgall; Paul Maria Scheikl; Anton Deguet; Brandon M. White; De Ru Tsai; Richard Jaepyeong Cha; Jeffrey Jopling; et al. (Sci. Robotics, 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 Fränken; et al. (CoRR, 2025)

FAST: Efficient Action Tokenization for Vision-Language-Action Models. - Karl Pertsch; Kyle Stachowicz; Brian Ichter; Danny Driess; Suraj Nair 003; Quan Vuong; Oier Mees; Chelsea Finn; Sergey Levine (CoRR, 2025)

MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation. - Haibo Tong; Zhaoyang Wang 004; Zhaorun Chen; Haonian Ji; Shi Qiu; Siwei Han; Kexin Geng; Zhongkai Xue; Yiyang Zhou; Peng Xia 005; Mingyu Ding; Rafael Rafailov; et al. (CoRR, 2025)

FSPO: Few-Shot Preference Optimization of Synthetic Preference Data in LLMs Elicits Effective Personalization to Real Users. - Anikait Singh; Sheryl Hsu; Kyle Hsu; Eric Mitchell; Stefano Ermon; Tatsunori Hashimoto; Archit Sharma; Chelsea Finn (CoRR, 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; et al. (CoRR, 2025)

Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success. - Moo Jin Kim; Chelsea Finn; Percy Liang (CoRR, 2025)

Curating Demonstrations using Online Experience. - Annie S. Chen; Alec M. Lessing; Yuejiang Liu; Chelsea Finn (CoRR, 2025)

π0.5: a Vision-Language-Action Model with Open-World Generalization. - Physical Intelligence; Kevin Black; Noah Brown; James Darpinian; Karan Dhabalia; Danny Driess; Adnan Esmail; Michael Equi; Chelsea Finn; Niccolo Fusai; Manuel Y. Galliker; Dibya Ghosh; et al. (CoRR, 2025)

Latent Diffusion Planning for Imitation Learning. - Amber Xie; Oleh Rybkin; Dorsa Sadigh; Chelsea Finn (CoRR, 2025)

What Matters for Batch Online Reinforcement Learning in Robotics? - Perry Dong; Suvir Mirchandani; Dorsa Sadigh; Chelsea Finn (CoRR, 2025)

Learning Long-Context Diffusion Policies via Past-Token Prediction. - Marcel Torne; Andy Tang; Yuejiang Liu; Chelsea Finn (CoRR, 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 Jaepyeong Cha; Jeffrey Jopling; et al. (CoRR, 2025)

Just Enough Thinking: Efficient Reasoning with Adaptive Length Penalties Reinforcement Learning. - Violet Xiang; Chase Blagden; Rafael Rafailov; Nathan Lile; Sang T. Truong; Chelsea Finn; Nick Haber (CoRR, 2025)

Reinforcement Learning via Implicit Imitation Guidance. - Perry Dong; Alec M. Lessing; Annie S. Chen; Chelsea Finn (CoRR, 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 S. Hu; Fabio Ramos 001; Jonathan Tremblay; Kanav Arora; et al. (CoRR, 2025)

EXPO: Stable Reinforcement Learning with Expressive Policies. - Perry Dong; Qiyang Li; Dorsa Sadigh; Chelsea Finn (CoRR, 2025)

Self-Guided Action Diffusion. - Rhea Malhotra; Yuejiang Liu; Chelsea Finn (CoRR, 2025)