Bio

Chelsea Finn is an Assistant Professor in Computer Science and Electrical Engineering at Stanford University, and the William George and Ida Mary Hoover Faculty Fellow. Professor Finn's research interests lie in the ability to enable robots and other agents to develop broadly intelligent behavior through learning and interaction. Her work lies at the intersection of machine learning and robotic control, including topics such as end-to-end learning of visual perception and robotic manipulation skills, deep reinforcement learning of general skills from autonomously collected data, and meta-learning algorithms that can enable fast learning of new concepts and behaviors. Professor Finn received her Bachelors degree in Electrical Engineering and Computer Science at MIT and her PhD in Computer Science at UC Berkeley. Her research has been recognized through the ACM doctoral dissertation award, the Presidential Early Career Award for Scientists and Engineers, and the MIT Technology Review 35 under 35 list, and her work has been covered by various media outlets, including the New York Times, Wired, and Bloomberg. Throughout her career, she has sought to increase the representation of underrepresented minorities within CS and AI by developing an AI outreach camp at Berkeley for underprivileged high school students, a mentoring program for underrepresented undergraduates across three universities, and leading efforts within the WiML and Berkeley WiCSE communities of women researchers.

Academic Appointments

Honors & Awards

  • Presidential Early Career Award for Scientists and Engineers, United States federal government (2025)
  • Research Fellowship, Alfred P. Sloan Foundation (2023)
  • Early Academic Career Award in Robotics and Automation, IEEE RAS (2022)
  • Young Investigator Award, Office of Naval Research (2021)
  • Microsoft Faculty Fellowship, Microsoft (2020)
  • ACM Doctoral Dissertation Award, ACM (2019)
  • 35 Under 35 Innovator, MIT Technology Review (2018)
  • C.V. Ramamoorthy Distinguished Research Award, UC Berkeley (2017)

Program Affiliations

  • Symbolic Systems Program

Contact

  • Academic
    cbfinn@cs.stanford.edu
    University - Faculty Department: Computer Science Position: Asst Professor
    University - Faculty Department: Electrical Engineering Position: Asst Professor
    • 353 JANE STANFORD WAY
    • STANFORD,  California  94305 

Additional Info

Links

2025-26 Courses

Stanford Advisees

All Publications

  • SRT-H: A hierarchical framework for autonomous surgery via language-conditioned imitation learning. Science robotics Kim, J. W., Chen, J. T., Hansen, P., Shi, L. X., Goldenberg, A., Schmidgall, S., Scheikl, P. M., Deguet, A., White, B. M., Tsai, D. R., Cha, R. J., Jopling, J., Finn, C., Krieger, A. 2025; 10 (104): eadt5254

    Abstract

    Research on autonomous surgery has largely focused on simple task automation in controlled environments. However, real-world surgical applications demand dexterous manipulation over extended durations and robust generalization to the inherent variability of human tissue. These challenges remain difficult to address using existing logic-based or conventional end-to-end learning strategies. To address this gap, we propose a hierarchical framework for performing dexterous, long-horizon surgical steps. Our approach uses a high-level policy for task planning and a low-level policy for generating low-level trajectories. The high-level planner plans in language space, generating task-level or corrective instructions that guide the robot through the long-horizon steps and help recover from errors made by the low-level policy. We validated our framework through ex vivo experiments on cholecystectomy, a commonly practiced minimally invasive procedure, and conducted ablation studies to evaluate key components of the system. Our method achieves a 100% success rate across eight different ex vivo gallbladders, operating fully autonomously without human intervention. The hierarchical approach improved the policy's ability to recover from suboptimal states that are inevitable in the highly dynamic environment of realistic surgical applications. This work demonstrates step-level autonomy in a surgical procedure, marking a milestone toward clinical deployment of autonomous surgical systems.

    View details for DOI 10.1126/scirobotics.adt5254

    View details for PubMedID 40632876

  • Commonsense Reasoning for Legged Robot Adaptation with Vision-Language Models Chen, A. S., Lessing, A. M., Tang, A., Chada, G., Smith, L., Levine, S., Finn, C. edited by Ott, C. IEEE. 2025: 12826-12833

    View details for DOI 10.1109/ICRA55743.2025.11127234

    View details for Web of Science ID 001614889900129

  • CoT-VLA: Visual Chain-of-Thought Reasoning for Vision-Language-Action Models Zhao, Q., Lu, Y., Kim, M., Fu, Z., Zhang, Z., Wu, Y., Li, Z., Ma, Q., Han, S., Finn, C., Handa, A., Lin, T., Wetzstein, G., Liu, M., Xiang, D., IEEE COMPUTER SOC IEEE COMPUTER SOC. 2025: 1702-1713

    View details for DOI 10.1109/CVPR52734.2025.00166

    View details for Web of Science ID 001562507802011

  • SPEEDTUNING: Speeding Up Policy Execution with Lightweight Reinforcement Learning Yuan, D. D., Zhao, T. Z., Burns, K., Finn, C. edited by Ott, C. IEEE. 2025: 1184-1192

    View details for DOI 10.1109/ICRA55743.2025.11128753

    View details for Web of Science ID 001582497400106

  • RoboCrowd: Scaling Robot Data Collection through Crowdsourcing Mirchandani, S., Yuan, D. D., Burns, K., Islam, M., Zhao, T. Z., Finn, C., Sadigh, D. edited by Ott, C. IEEE. 2025: 1392-1399

    View details for DOI 10.1109/ICRA55743.2025.11127402

    View details for Web of Science ID 001582497400129

  • A Tutorial on Meta-Reinforcement Learning FOUNDATIONS AND TRENDS IN MACHINE LEARNING Beck, J., Vuorio, R., Liu, E., Xiong, Z., Zintgraf, L., Finn, C., Whiteson, S. 2025; 18 (2-3)

    View details for DOI 10.1561/2200000080

    View details for Web of Science ID 001462355200001

  • Bayesian Embeddings for Few-Shot Open World Recognition. IEEE transactions on pattern analysis and machine intelligence Willes, J., Harrison, J., Harakeh, A., Finn, C., Pavone, M., Waslander, S. L. 2024; 46 (3): 1513-1529

    Abstract

    As autonomous decision-making agents move from narrow operating environments to unstructured worlds, learning systems must move from a closed-world formulation to an open-world and few-shot setting in which agents continuously learn new classes from small amounts of information. This stands in stark contrast to modern machine learning systems that are typically designed with a known set of classes and a large number of examples for each class. In this work we extend embedding-based few-shot learning algorithms to the open-world recognition setting. We combine Bayesian non-parametric class priors with an embedding-based pre-training scheme to yield a highly flexible framework which we refer to as few-shot learning for open world recognition (FLOWR). We benchmark our framework on open-world extensions of the common MiniImageNet and TieredImageNet few-shot learning datasets. Our results show, compared to prior methods, strong classification accuracy performance and up to a 12% improvement in H-measure (a measure of novel class detection) from our non-parametric open-world few-shot learning scheme.

    View details for DOI 10.1109/TPAMI.2022.3201541

    View details for PubMedID 36063507

  • A Fast and Accurate Machine Learning Autograder for the Breakout Assignment Liu, E., Yuan, D., Ahmed, A., Cornwall, E., Woodrow, J., Burns, K., Nie, A., Brunskill, E., Piech, C., Assoc Computing Machinery ASSOC COMPUTING MACHINERY. 2024: 736-742

    View details for DOI 10.1145/3626252.3630759

    View details for Web of Science ID 001181240800108

  • Evaluating Real-World Robot Manipulation Policies in Simulation Li, X., Hsu, K., Gu, J., Pertsch, K., Mees, O., Walke, H., Fu, C., Lunawat, I., Sieh, I., Kirmani, S., Levine, S., Wu, J., Finn, C., Su, H., Vuong, Q., Xiao, T. edited by Kroemer, O., Agrawal, P., Burgard, W. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2024

    View details for Web of Science ID 001483833800174

  • HumanPlus: Humanoid Shadowing and Imitation from Humans Fu, Z., Zhao, Q., Wu, Q., Wetzstein, G., Finn, C. edited by Kroemer, O., Agrawal, P., Burgard, W. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2024

    View details for Web of Science ID 001483833800128

  • OpenVLA: An Open-Source Vision-Language-Action Model Kim, M., Pertsch, K., Karamcheti, S., Xiao, T., Balakrishna, A., Nair, S., Rafailov, R., Foster, E., Sanketi, P., Vuong, Q., Kollar, T., Burchfiel, B., Tedrake, R., Sadigh, D., Levine, S., Liang, P., Finn, C. edited by Kroemer, O., Agrawal, P., Burgard, W. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2024

    View details for Web of Science ID 001483833800122

  • What Makes Pre-Trained Visual Representations Successful for Robust Manipulation? Burns, K., Witzel, Z., Ibn Hamid, J., Yu, T., Finn, C., Hausman, K. edited by Kroemer, O., Agrawal, P., Burgard, W. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2024

    View details for Web of Science ID 001483833800218

  • Mobile ALOHA: Learning Bimanual Mobile Manipulation using Low-Cost Whole-Body Teleoperation Fu, Z., Zhao, T. Z., Finn, C. edited by Kroemer, O., Agrawal, P., Burgard, W. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2024

    View details for Web of Science ID 001483833800193

  • Robotic Control via Embodied Chain-of-Thought Reasoning Zawalski, M., Chen, W., Pertsch, K., Mees, O., Finn, C., Levine, S. edited by Kroemer, O., Agrawal, P., Burgard, W. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2024

    View details for Web of Science ID 001483833800146

  • Decomposing the Generalization Gap in Imitation Learning for Visual Robotic Manipulation Xie, A., Lee, L., Xiao, T., Finn, C., IEEE IEEE. 2024: 3153-3160

    View details for DOI 10.1109/ICRA57147.2024.10611331

    View details for Web of Science ID 001294576202086

  • Efficient Imitation Learning with Conservative World Models Kolev, V., Rafailov, R., Hatch, K., Wu, J., Finn, C. edited by Abate, A., Cannon, M., Margellos, K., Papachristodoulou, A. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2024: 1776-1789

    View details for Web of Science ID 001347137500136

  • Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning Yang, J., Mark, M., Vu, B., Sharma, A., Bohg, J., Finn, C., IEEE IEEE. 2024: 4804-4811

    View details for DOI 10.1109/ICRA57147.2024.10610421

    View details for Web of Science ID 001294576203104

  • PIGEON: Predicting Image Geolocations Haas, L., Skreta, M., Alberti, S., Finn, C., IEEE IEEE COMPUTER SOC. 2024: 12893-12902

    View details for DOI 10.1109/CVPR52733.2024.01225

    View details for Web of Science ID 001342442404025

  • SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning Luo, J., Hu, Z., Xu, C., Tan, Y., Berg, J., Sharma, A., Schaal, S., Finn, C., Gupta, A., Levine, S., IEEE IEEE. 2024: 16961-16969

    View details for DOI 10.1109/ICRA57147.2024.10610040

    View details for Web of Science ID 001369728005136

  • Disentangling Length from Quality in Direct Preference Optimization Park, R., Rafailov, R., Ermon, S., Finn, C. edited by Martins, A., Srikumar, Ku, L. W. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2024: 4998-5017

    View details for Web of Science ID 001356731805009

  • Clarify: Improving Model Robustness With Natural Language Corrections Lee, Y., Lam, M. S., Vasconcelos, H., Bernstein, M. S., Finn, C., ACM ASSOC COMPUTING MACHINERY. 2024

    View details for DOI 10.1145/3654777.3676362

    View details for Web of Science ID 001336570000040

  • Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks Kim, J., Zhao, T. Z., Schmidgall, S., Deguet, A., Kobilarov, M., Finn, C., Krieger, A. edited by Kroemer, O., Agrawal, P., Burgard, W. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2024

    View details for Web of Science ID 001483833800007

  • Open X-Embodiment: Robotic Learning Datasets and RT-X Models O'Neill, A., Rehman, A., Gupta, A., Maddukuri, A., Gupta, A., Padalkar, A., Lee, A., Pooley, A., Gupta, A., Mandlekar, A., Jain, A., Tung, A., Bewley, A., Herzog, A., Irpan, A., Khazatsky, A., Rai, A., Gupta, A., Wang, A., Kolobov, A., Singh, A., Garg, A., Kembhavi, A., Xie, A., Brohan, A., Raffin, A., Sharma, A., Yavary, A., Jain, A., Balakrishna, A., Wahid, A., Burgess-Limerick, B., Kim, B., Scholkopf, B., Wulfe, B., Ichter, B., Lu, C., Xu, C., Le, C., Finn, C., Wang, C., Xu, C., Chi, C., Huang, C., Chan, C., Agia, C., Pan, C., Fu, C., Devin, C., Xu, D., Morton, D., Driess, D., Chen, D., Pathak, D., Shah, D., Buchler, D., Jayaraman, D., Kalashnikov, D., Sadigh, D., Johns, E., Foster, E., Liu, F., Ceola, F., Xia, F., Zhao, F., Frujeri, F., Stulp, F., Zhou, G., Sukhatme, G. S., Salhotra, G., Yan, G., Feng, G., Schiavi, G., Berseth, G., Kahn, G., Yang, G., Wang, G., Su, H., Fang, H., Shi, H., Bao, H., Ben Amor, H., Christensen, H., Furuta, H., Bharadhwaj, H., Walke, H., Fang, H., Ha, H., Mordatch, I., Radosavovic, I., Leal, I., Liang, J., Abou-Chakra, J., Kim, J., Drake, J., Peters, J., Schneider, J., Hsu, J., Vakil, J., Bohg, J., Bingham, J., Wu, J., Gao, J., Hu, J., Wu, J., Wu, J., Sun, J., Luo, J., Gu, J., Tan, J., Oh, J., Wu, J., Lu, J., Yang, J., Malik, J., Silverio, J., Hejna, J., Booher, J., Tompson, J., Yang, J., Salvador, J., Lim, J. J., Han, J., Wang, K., Rao, K., Pertsch, K., Hausman, K., Go, K., Gopalakrishnan, K., Goldberg, K., Byrne, K., Oslund, K., Kawaharazuka, K., Black, K., Lin, K., Zhang, K., Ehsani, K., Lekkala, K., Ellis, K., Rana, K., Srinivasan, K., Fang, K., Singh, K., Zeng, K., Hatch, K., Hsu, K., Itti, L., Chen, L., Pinto, L., Li Fei-Fei, Tan, L., Fan, L., Ott, L., Lee, L., Weihs, L., Chen, M., Lepert, M., Memmel, M., Tomizuka, M., Itkina, M., Castro, M., Spero, M., Du, M., Ahn, M., Yip, M. C., Zhang, M., Ding, M., Heo, M., Srirama, M., Sharma, M., Kim, M., Kanazawa, N., Hansen, N., Heess, N., Joshi, N. J., Suenderhauf, N., Liu, N., Di Palo, N., Shafiullah, N., Mees, O., Kroemer, O., Bastani, O., Sanketi, P. R., Miller, P., Yin, P., Wohlhart, P., Xu, P., Fagan, P., Mitrano, P., Sermanet, P., Abbeel, P., Sundaresan, P., Chen, Q., Vuong, Q., Rafailov, R., Tian, R., Doshi, R., Martin-Martin, R., Baijal, R., Scalise, R., Hendrix, R., Lin, R., Qian, R., Zhang, R., Mendonca, R., Shah, R., Hoque, R., Julian, R., Bustamante, S., Kirmani, S., Levine, S., Lin, S., Moore, S., Bahl, S., Dass, S., Sonawani, S., Tulsiani, S., Song, S., Xu, S., Haldar, S., Karamcheti, S., Adebola, S., Guist, S., Nasiriany, S., Schaal, S., Welker, S., Tian, S., Ramamoorthy, S., Dasari, S., Belkhale, S., Park, S., Nair, S., Mirchandani, S., Osa, T., Gupta, T., Harada, T., Matsushima, T., Xiao, T., Kollar, T., Yu, T., Ding, T., Davchev, T., Zhao, T. Z., Armstrong, T., Darrell, T., Chung, T., Jain, V., Kumar, V., Vanhoucke, V., Zhan, W., Zhou, W., Burgard, W., Chen, X., Chen, X., Wang, X., Zhu, X., Geng, X., Liu, X., Xu Liangwei, Li, X., Pang, Y., Lu, Y., Ma, Y., Kim, Y., Chebotar, Y., Zhou, Y., Zhu, Y., Wu, Y., Xu, Y., Wang, Y., Bisk, Y., Dou, Y., Cho, Y., Lee, Y., Cui, Y., Cao, Y., Wu, Y., Tang, Y., Zhu, Y., Zhang, Y., Jiang, Y., Li, Y., Li, Y., Iwasawa, Y., Matsuo, Y., Ma, Z., Xu, Z., Cui, Z., Zhang, Z., Fu, Z., Lin, Z., IEEE IEEE. 2024: 6892-6903

    View details for DOI 10.1109/ICRA57147.2024.10611477

    View details for Web of Science ID 001294576205021

  • Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models Henderson, P., Mitchell, E., Manning, C. D., Jurafsky, D., Finn, C., ACM ASSOC COMPUTING MACHINERY. 2023: 287-296

    View details for DOI 10.1145/3600211.3604690

    View details for Web of Science ID 001117838100023

  • Contrastive Example-Based Control Hatch, K., Eysenbach, B., Rafailov, R., Yu, T., Salakhutdinov, R., Levine, S., Finn, C. edited by Pappas, G. J., Matni, N., Morari, M. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2023

    View details for Web of Science ID 001221742900013

  • Robot Parkour Learning Zhuang, Z., Fu, Z., Wang, J., Atkeson, C., Schwertfeger, S., Finn, C., Zhao, H. edited by Tan, J., Toussaint, M., Darvish, K. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2023

    View details for Web of Science ID 001221201500003

  • RoboCLIP: One Demonstration is Enough to Learn Robot Policies Sontakke, S. A., Zhang, J., Arnold, S. M. R., Pertsch, K., Biyik, E., Sadigh, D., Finn, C., Itti, L. edited by Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

    View details for Web of Science ID 001228825105002

  • Waypoint-Based Imitation Learning for Robotic Manipulation Shi, L., Sharma, A., Zhao, T. Z., Finn, C. edited by Tan, J., Toussaint, M., Darvish, K. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2023

    View details for Web of Science ID 001221201502013

  • Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning Sharma, A., Ahmed, A. M., Ahmad, R., Finn, C. edited by Tan, J., Toussaint, M., Darvish, K. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2023

    View details for Web of Science ID 001221201503019

  • MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning Rafailov, R., Hatch, K., Kolev, V., Martin, J. D., Phielipp, M., Finn, C. edited by Tan, J., Toussaint, M., Darvish, K. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2023

    View details for Web of Science ID 001221201503038

  • Polybot: Training One Policy Across Robots While Embracing Variability Yang, J., Sadigh, D., Finn, C. edited by Tan, J., Toussaint, M., Darvish, K. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2023

    View details for Web of Science ID 001221201503002

  • BridgeData V2: A Dataset for Robot Learning at Scale Walke, H., Black, K., Lee, A., Kim, M., Du, M., Zheng, C., Zhao, T., Hansen-Estruch, P., Vuong, Q., He, A., Myers, V., Fang, K., Finn, C., Levine, S. edited by Tan, J., Toussaint, M., Darvish, K. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2023

    View details for Web of Science ID 001221201501041

  • Disentanglement via Latent Quantization Hsu, K., Dorrell, W., Whittington, J. C. R., Wu, J., Finn, C. edited by Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

    View details for Web of Science ID 001220818801008

  • Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning Nakamoto, M., Zhai, Y., Singh, A., Mark, M., Ma, Y., Finn, C., Kumar, A., Levine, S. edited by Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

    View details for Web of Science ID 001220600001015

  • Neural Functional Transformers Zhou, A., Yang, K., Jiang, Y., Burns, K., Xu, W., Sokota, S., Kolter, J., Finn, C. edited by Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

    View details for Web of Science ID 001227224000011

  • Permutation Equivariant Neural Functionals Zhou, A., Yang, K., Burns, K., Cardace, A., Jiang, Y., Sokota, S., Kolter, J., Finn, C. edited by Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

    View details for Web of Science ID 001227224007034

  • Train Offline, Test Online: A Real Robot Learning Benchmark Zhou, G., Dean, V., Srirama, M., Rajeswaran, A., Pari, J., Hatch, K., Jain, A., Yu, T., Abbeel, P., Pinto, L., Finn, C., Gupta, A., IEEE IEEE. 2023: 9197-9203

    View details for DOI 10.1109/ICRA48891.2023.10160594

    View details for Web of Science ID 001048371101123

  • NeRF in the Palm of Your Hand: Corrective Augmentation for Robotics via Novel-View Synthesis Zhou, A., Kim, M., Wang, L., Florence, P., Finn, C., IEEE IEEE COMPUTER SOC. 2023: 17907-17917

    View details for DOI 10.1109/CVPR52729.2023.01717

    View details for Web of Science ID 001062531302021

  • Supervised Pretraining Can Learn In-Context Reinforcement Learning Lee, J. N., Xie, A., Pacchiano, A., Chandak, Y., Finn, C., Nachum, O., Brunskill, E. edited by Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

    View details for Web of Science ID 001229826601007

  • Direct Preference Optimization: Your Language Model is Secretly a Reward Model Rafailov, R., Sharma, A., Mitchell, E., Ermon, S., Manning, C. D., Finn, C. edited by Oh, A., Neumann, T., Globerson, A., Saenko, K., Hardt, M., Levine, S. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023

    View details for Web of Science ID 001220818800032

  • Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning Du, M., Lee, O. Y., Nair, S., Finn, C. edited by Hauser, K., Shell, D., Huang, S. RSS FOUNDATION-ROBOTICS SCIENCE & SYSTEMS FOUNDATION. 2022

    View details for Web of Science ID 000827625700009

  • A State-Distribution Matching Approach to Non-Episodic Reinforcement Learning Sharma, A., Ahmad, R., Finn, C. edited by Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022: 19645-19657

    View details for Web of Science ID 000900130200031

  • Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlations Zhang, M., Sohoni, N. S., Zhang, H. R., Finn, C., Re, C. edited by Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022

    View details for Web of Science ID 000900130207034

  • Bridge Data: Boosting Generalization of Robotic Skills with Cross-Domain Datasets Ebert, F., Yang, Y., Schmeckpeper, K., Bucher, B., Georgakis, G., Daniilidis, K., Finn, C., Levine, S. edited by Hauser, K., Shell, D., Huang, S. RSS FOUNDATION-ROBOTICS SCIENCE & SYSTEMS FOUNDATION. 2022

    View details for Web of Science ID 000827625700063

  • Memory-Based Model Editing at Scale Mitchell, E., Lin, C., Bosselut, A., Manning, C. D., Finn, C. edited by Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022

    View details for Web of Science ID 000900064905041

  • Robust Policy Learning over Multiple Uncertainty Sets Xie, A., Sodhani, S., Finn, C., Pineau, J., Zhang, A. edited by Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022

    View details for Web of Science ID 000900130205028

  • How to Leverage Unlabeled Data in Offline Reinforcement Learning Yu, T., Kumar, A., Chebotar, Y., Hausman, K., Finn, C., Levine, S. edited by Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022

    View details for Web of Science ID 000900130206039

  • Improving Out-of-Distribution Robustness via Selective Augmentation Yao, H., Wang, Y., Li, S., Zhang, L., Liang, W., Zou, J., Finn, C. edited by Chaudhuri, K., Jegelka, S., Song, L., Szepesvari, C., Niu, G., Sabato, S. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022

    View details for Web of Science ID 000900130206027

  • Training and Evaluation of Deep Policies Using Reinforcement Learning and Generative Models JOURNAL OF MACHINE LEARNING RESEARCH Ghadirzadeh, A., Poklukar, P., Arndt, K., Finn, C., Kyrki, V., Kragic, D., Bjorkman, M. 2022; 23

    View details for Web of Science ID 001003314000001

  • Batch Exploration With Examples for Scalable Robotic Reinforcement Learning IEEE ROBOTICS AND AUTOMATION LETTERS Chen, A. S., Nam, H., Nair, S., Finn, C. 2021; 6 (3): 4401–8

    View details for DOI 10.1109/LRA.2021.3068655

    View details for Web of Science ID 000639767800019

  • Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones IEEE ROBOTICS AND AUTOMATION LETTERS Thananjeyan, B., Balakrishna, A., Nair, S., Luo, M., Srinivasan, K., Hwang, M., Gonzalez, J. E., Ibarz, J., Finn, C., Goldberg, K. 2021; 6 (3): 4915-4922

    View details for DOI 10.1109/LRA.2021.3070252

    View details for Web of Science ID 000642765100002

  • How to train your robot with deep reinforcement learning: lessons we have learned INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH Ibarz, J., Tan, J., Finn, C., Kalakrishnan, M., Pastor, P., Levine, S. 2021; 40 (4-5): 698-721

    View details for DOI 10.1177/0278364920987859

    View details for Web of Science ID 000648404100003

  • WILDS: A Benchmark of in-the-Wild Distribution Shifts Koh, P., Sagawa, S., Marklund, H., Xie, S., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R., Gao, I., Lee, T., David, E., Stavness, I., Guo, W., Earnshaw, B. A., Haque, I. S., Beery, S., Leskovec, J., Kundaje, A., Pierson, E., Levine, S., Finn, C., Liang, P. edited by Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

    View details for Web of Science ID 000683104605062

  • Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms Ghadirzadeh, A., Chen, X., Poklukar, P., Finn, C., Bjorkman, M., Kragic, D., IEEE IEEE. 2021: 1274-1280

    View details for DOI 10.1109/IROS51168.2021.9636628

    View details for Web of Science ID 000755125501008

  • Offline Meta-Reinforcement Learning with Advantage Weighting Mitchell, E., Rafailov, R., Peng, X., Levine, S., Finn, C. edited by Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

    View details for Web of Science ID 000768182703084

  • Deep Reinforcement Learning amidst Continual Structured Non-Stationarity Xie, A., Harrison, J., Finn, C. edited by Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

    View details for Web of Science ID 000768182701045

  • Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videos Chen, A. S., Nair, S., Finn, C. edited by Shell, D. A., Toussaint, M., Hsieh, M. A. RSS FOUNDATION-ROBOTICS SCIENCE & SYSTEMS FOUNDATION. 2021

    View details for Web of Science ID 000684604200012

  • Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices Liu, E., Raghunathan, A., Liang, P., Finn, C. edited by Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

    View details for Web of Science ID 000683104606087

  • Just Train Twice: Improving Group Robustness without Training Group Information Liu, E., Haghgoo, B., Chen, A. S., Raghunathan, A., Koh, P., Sagawa, S., Liang, P., Finn, C. edited by Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

    View details for Web of Science ID 000683104606074

  • Catformer: Designing Stable Transformers via Sensitivity Analysis Davis, J., Gu, A., Choromanski, K., Dao, T., Re, C., Finn, C., Liang, P. edited by Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

    View details for Web of Science ID 000683104602046

  • Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills Chebotar, Y., Hausman, K., Lu, Y., Xiao, T., Kalashnikov, D., Varley, J., Irpan, A., Eysenbach, B., Julian, R., Finn, C., Levine, S. edited by Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

    View details for Web of Science ID 000683104601047

  • Greedy Hierarchical Variational Autoencoders for Large-Scale Video Prediction Wu, B., Nair, S., Martin-Martin, R., Li Fei-Fei, Finn, C., IEEE COMP SOC IEEE COMPUTER SOC. 2021: 2318-2328

    View details for DOI 10.1109/CVPR46437.2021.00235

    View details for Web of Science ID 000739917302051

  • Scalable Multi-Task Imitation Learning with Autonomous Improvement Singh, A., Jang, E., Irpan, A., Kappler, D., Dalal, M., Levinev, S., Khansari, M., Finn, C., IEEE IEEE. 2020: 2167-2173

    View details for Web of Science ID 000712319501084

  • OmniTact: A Multi-Directional High-Resolution Touch Sensor Padmanabha, A., Ebert, F., Tian, S., Calandra, R., Finn, C., Levine, S., IEEE IEEE. 2020: 618-624

    View details for Web of Science ID 000712319500072

  • Meta-Inverse Reinforcement Learning with Probabilistic Context Variables Yu, L., Yu, T., Finn, C., Ermon, S. edited by Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

    View details for Web of Science ID 000535866903039

  • Unsupervised Curricula for Visual Meta-Reinforcement Learning Jabri, A., Hsu, K., Eysenbach, B., Gupta, A., Levine, S., Finn, C. edited by Wallach, H., Larochelle, H., Beygelzimer, A., d'Alche-Buc, F., Fox, E., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2019

    View details for Web of Science ID 000535866902018

  • Unsupervised Visuomotor Control through Distributional Planning Networks Yu, T., Shevchuk, G., Sadigh, D., Finn, C. edited by Bicchi, A., KressGazit, H., Hutchinson, S. MIT PRESS. 2019

    View details for Web of Science ID 000570976800020

  • One-Shot Composition of Vision-Based Skills from Demonstration Yu, T., Abbeel, P., Levine, S., Finn, C., IEEE IEEE. 2019: 2643–50

    View details for Web of Science ID 000544658402035

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