Chelsea Finn

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

• 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

• RoboArena: Distributed Real-World Evaluation of Generalist Robot Policies Atreya, P., Pertsch, K., Lee, T., Kim, M., Jain, A., Kuramshin, A., Eppner, C., Neary, C., Hub, E., Ramos, F., Tremblay, J., Arora, K., Ellis, K., Macesanu, L., Leonard, M., Cho, M., Aslan, O., Dass, S., Wang, J., Yuan, X., Yang, X., Gupta, A., Jayaraman, D., Berseth, G., Daniilidis, K., Martin-Martin, R., Lee, Y., Liang, P., Finn, C., Levine, S. edited by Lim, J., Song, S., Park, H. W. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2025: 336-364

  View details for Web of Science ID 001676333500016

• 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

• ADAPT ON-THE-GO: BEHAVIOR MODULATION FOR SINGLE- LIFE ROBOT DEPLOYMENT Chen, A. S., Chada, G., Smith, L., Sharma, A., Fu, Z., Levine, S., Finn, C. edited by Chandar, S., Pascanu, R., Eaton, E., Liu, B., Mahmood, R., Rannen-Triki, A. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2025

  View details for Web of Science ID 001865181400014

• Helpful DoggyBot: Open-World Object Fetching using Legged Robots and Vision-Language Models Wu, Q., Fu, Z., Cheng, X., Wang, X., Finn, C. edited by Laugier, C., Atanasov, N., Birchfield, S., Cielniak, G., DeMattos, L., Fiorini, L., Giguere, P., Hashimoto, K., Ibanez-Guzman, J., Kamegawa, T., Lee, J., Laugier, C., Loianno, G., Luck, K., Maruyama, H., Martinet, P., Moradi, H., Nunes, U., Pettre, J., Pretto, A., Ranzani, T., Ronnau, A., Rossi, S., Rouse, E., Ruggiero, F., Simonin, O., Wang, D., Yang, M., Yoshida, E., Zhao, H. IEEE. 2025: 8826-8833

  View details for DOI 10.1109/IROS60139.2025.11246368

  View details for Web of Science ID 001734964700178

• 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

• 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

• Latent Diffusion Planning for Imitation Learning Xie, A., Rybkin, O., Sadigh, D., Finn, C. edited by Singh, A., Fazel, M., Hsu, D., Lacoste-Julien, S., Berkenkamp, F., Maharaj, T., Wagstaff, K., Zhu, J. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2025: 68710-68724

  View details for Web of Science ID 001693170600042

• Affordance-Guided Reinforcement Learning via Visual Prompting Lee, O. Y., Xie, A., Fang, K., Pertsch, K., Finn, C. edited by Laugier, C., Atanasov, N., Birchfield, S., Cielniak, G., DeMattos, L., Fiorini, L., Giguere, P., Hashimoto, K., Ibanez-Guzman, J., Kamegawa, T., Lee, J., Laugier, C., Loianno, G., Luck, K., Maruyama, H., Martinet, P., Moradi, H., Nunes, U., Pettre, J., Pretto, A., Ranzani, T., Ronnau, A., Rossi, S., Rouse, E., Ruggiero, F., Simonin, O., Wang, D., Yang, M., Yoshida, E., Zhao, H. IEEE. 2025: 2352-2359

  View details for DOI 10.1109/IROS60139.2025.11246854

  View details for Web of Science ID 001727409500247

• Hi Robot: Open-Ended Instruction Following with Hierarchical Vision-Language-Action Models Shi, L., Ichter, B., Equi, M., Ke, L., Pertsch, K., Vuong, Q., Tanner, J., Walling, A., Wang, H., Fusai, N., Li-Bell, A., Driess, D., Groom, L., Levine, S., Finn, C. edited by Singh, A., Fazel, M., Hsu, D., Lacoste-Julien, S., Berkenkamp, F., Maharaj, T., Wagstaff, K., Zhu, J. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2025: 54919-54933

  View details for Web of Science ID 001693164800080

• 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

• 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

• Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms Rafailov, R., Chittepu, Y., Park, R., Sikchi, H., Hejna, J., Knox, W., Finn, C., Niekum, S. edited by Globerson, A., Mackey, L., Belgrave, D., Fan, A., Paquet, U., Tomczak, J., Zhang, C. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2024

  View details for Web of Science ID 001633239900134

• A Critical Evaluation of AI Feedback for Aligning Large Language Models Sharma, A., Keh, S., Mitchell, E., Finn, C., Arora, K., Kollar, T. edited by Globerson, A., Mackey, L., Belgrave, D., Fan, A., Paquet, U., Tomczak, J., Zhang, C. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2024

  View details for Web of Science ID 001595165600070

• Universal Neural Functionals Zhou, A., Finn, C., Harrison, J. edited by Globerson, A., Mackey, L., Belgrave, D., Fan, A., Paquet, U., Tomczak, J., Zhang, C. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2024

  View details for Web of Science ID 001633259400036

• 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

• 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

• 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

• 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

• Meta-Learning Online Adaptation of Language Models Hu, N., Mitchell, E., Manning, C. D., Finn, C. edited by Bouamor, H., Pino, J., Bali, K. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2023: 4418-4432

  View details for Web of Science ID 001275019904034

• 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

• 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

• 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

• 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

• 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

• 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

• 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

• 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

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• LAPO: Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning Chen, X., Ghadirzadeh, A., Yu, T., Wang, J., Gao, Y., Li, W., Liang, B., Finn, C., Zhang, C. edited by Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2022

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• Offline Reinforcement Learning at Multiple Frequencies Burns, K., Yu, T., Finn, C., Hausman, K. edited by Liu, K., Kulic, D., Ichnowski, J. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022: 2041-2051

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• You Only Live Once: Single-Life Reinforcement Learning Chen, A. S., Sharma, A., Levine, S., Finn, C. edited by Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2022

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• MEMO: Test Time Robustness via Adaptation and Augmentation Zhang, M., Levine, S., Finn, C. edited by Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2022

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• C-Mixup: Improving Generalization in Regression Yao, H., Wang, Y., Zhang, L., Zou, J., Finn, C. edited by Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2022

  View details for Web of Science ID 001213811601044

• Wild-Time: A Benchmark of in-the-Wild Distribution Shift over Time Yao, H., Choi, C., Cao, B., Lee, Y., Koh, P., Finn, C. edited by Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2022

  View details for Web of Science ID 001213811609063

• Giving Feedback on Interactive Student Programs with Meta-Exploration Liu, E., Stephan, M., Nie, A., Piech, C., Brunskill, E., Finn, C. edited by Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2022

  View details for Web of Science ID 001215469503049

• Learning Options via Compression Jiang, Y., Liu, E., Eysenbach, B., Kolter, J., Finn, C. edited by Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2022

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• Enhancing Self-Consistency and Performance of Pre-Trained Language Models through Natural Language Inference Mitchell, E., Noh, J. J., Li, S., Armstrong, W. S., Agarwal, A., Liu, P., Finn, C., Manning, C. D. edited by Goldberg, Y., Kozareva, Z., Zhang, Y. ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2022: 1754-1768

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• LIFELONG ROBOTIC REINFORCEMENT LEARNING BY RETAINING EXPERIENCES Xie, A., Finn, C. edited by Chandar, S., Pascanu, R., Precup, D. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022

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• When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning Xie, A., Tajwar, F., Sharma, A., Finn, C. edited by Koyejo, S., Mohamed, S., Agarwal, A., Belgrave, D., Cho, K., Oh, A. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2022

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• R3M: A Universal Visual Representation for Robot Manipulation Nair, S., Rajeswaran, A., Kumar, V., Finn, C., Gupta, A. edited by Liu, K., Kulic, D., Ichnowski, J. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2022: 892-909

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• 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

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• 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

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• 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

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• 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

• 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

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• 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

• 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

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• 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

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• 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

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• Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation Nair, S., Mitchell, E., Chen, K., Ichter, B., Savarese, S., Finn, C. edited by Faust, A., Hsu, D., Neumann, G. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021: 1303-1315

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• A Workflow for Offline Model-Free Robotic Reinforcement Learning Kumar, A., Singh, A., Tian, S., Finn, C., Levine, S. edited by Faust, A., Hsu, D., Neumann, G. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021: 417-428

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• Meta-learning with an Adaptive Task Scheduler Yao, H., Wang, Y., Wei, Y., Zhao, P., Mahdavi, M., Lian, D., Finn, C. edited by Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S., Vaughan, J. W. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2021

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• Conservative Data Sharing for Multi-Task Offline Reinforcement Learning Yu, T., Kumar, A., Chebotar, Y., Hausman, K., Levine, S., Finn, C. edited by Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S., Vaughan, J. W. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2021

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• COMBO: Conservative Offline Model-Based Policy Optimization Yu, T., Kumar, A., Rafailov, R., Rajeswaran, A., Levine, S., Finn, C. edited by Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S., Vaughan, J. W. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2021

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• Visual Adversarial Imitation Learning using Variational Models Rafailov, R., Yu, T., Rajeswaran, A., Finn, C. edited by Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S., Vaughan, J. W. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2021

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• Offline Reinforcement Learning from Images with Latent Space Models Rafailov, R., Yu, T., Rajeswaran, A., Finn, C. edited by Jadbabaie, A., Lygeros, J., Pappas, G. J., Parrilo, P. A., Recht, B., Tomlin, C. J., Zeilinger, M. N. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

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• Information is Power: Intrinsic Control via Information Capture Rhinehart, N., Wang, J., Berseth, G., Co-Reyes, J. D., Hafner, D., Finn, C., Levine, S. edited by Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S., Vaughan, J. W. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2021

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• Adaptive Risk Minimization: Learning to Adapt to Domain Shift Zhang, M., Marklund, H., Dhawan, N., Gupta, A., Levine, S., Finn, C. edited by Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S., Vaughan, J. W. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2021

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• Differentiable Annealed Importance Sampling and the Perils of Gradient Noise Zhang, G., Hsu, K., Li, J., Finn, C., Grosse, R. edited by Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S., Vaughan, J. W. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2021

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• Autonomous Reinforcement Learning via Subgoal Curricula Sharma, A., Gupta, A., Levine, S., Hausman, K., Finn, C. edited by Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S., Vaughan, J. W. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2021

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• BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning Jang, E., Irpan, A., Khansari, M., Kappler, D., Ebert, F., Lynch, C., Levine, S., Finn, C. edited by Faust, A., Hsu, D., Neumann, G. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021: 991-1002

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• Noether Networks: Meta-Learning Useful Conserved Quantities Alet, F., Doblar, D., Zhou, A., Tenenbaum, J., Kawaguchi, K., Finn, C. edited by Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P. S., Vaughan, J. W. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2021

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• Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks Wu, B., Nair, S., Li Fei-Fei, Finn, C. edited by Faust, A., Hsu, D., Neumann, G. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021: 1-13

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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

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• Gradient Surgery for Multi-Task Learning Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., Finn, C. edited by Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. F., Lin, H. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2020

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• MOPO: Model-based Offline Policy Optimization Yu, T., Thomas, G., Yu, L., Ermon, S., Zou, J., Levine, S., Finn, C., Ma, T. edited by Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. F., Lin, H. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2020

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• Learning Latent Representations to Influence Multi-Agent Interaction Xie, A., Losey, D. P., Tolsma, R., Finn, C., Sadigh, D. edited by Kober, J., Ramos, F., Tomlin, C. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2020: 575-588

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• Weakly-Supervised Reinforcement Learning for Controllable Behavior Lee, L., Eysenbach, B., Salakhutdinov, R., Gu, S., Finn, C. edited by Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. F., Lin, H. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2020

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• Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors Pertsch, K., Rybkin, O., Ebert, F., Finn, C., Jayaraman, D., Levine, S. edited by Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. F., Lin, H. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2020

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• Reinforcement Learning with Videos: Combining Offline Observations with Interaction Schmeckpeper, K., Rybkin, O., Daniilidis, K., Levine, S., Finn, C. edited by Kober, J., Ramos, F., Tomlin, C. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2020: 339-354

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• Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning Julian, R., Swanson, B., Sukhatme, G. S., Levine, S., Finn, C., Hausman, K. edited by Kober, J., Ramos, F., Tomlin, C. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2020: 2120-2136

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• MELD: Meta-Reinforcement Learning from Images via Latent State Models Zhao, T. Z., Nagabandi, A., Rakelly, K., Finn, C., Levine, S. edited by Kober, J., Ramos, F., Tomlin, C. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2020: 1246-1261

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• Learning Predictive Models from Observation and Interaction Schmeckpeper, K., Xie, A., Rybkin, O., Tian, S., Daniilidis, K., Levine, S., Finn, C. edited by Bischof, H., Brox, T., Frahm, J. M., Vedaldi, A. SPRINGER INTERNATIONAL PUBLISHING AG. 2020: 708-725

  View details for DOI 10.1007/978-3-030-58565-5_42

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• One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL Kumar, S., Kumar, A., Levine, S., Finn, C. edited by Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M. F., Lin, H. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2020

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• 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

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• 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

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• 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

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• 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

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

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• RoboNet: Large-Scale Multi-Robot Learning Dasari, S., Ebert, F., Tian, S., Nair, S., Bucher, B., Schmeckpeper, K., Singh, S., Levine, S., Finn, C. edited by Kaelbling, L. P., Kragic, D., Sugiura, K. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2019

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• Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning Yu, T., Quillen, D., He, Z., Julian, R., Hausman, K., Finn, C., Levine, S. edited by Kaelbling, L. P., Kragic, D., Sugiura, K. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2019

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• Entity Abstraction in Visual Model-Based Reinforcement Learning Veerapaneni, R., Co-Reyes, J. D., Chang, M., Janner, M., Finn, C., Wu, J., Tenenbaum, J., Levine, S. edited by Kaelbling, L. P., Kragic, D., Sugiura, K. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2019

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