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 experience, 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, an NSF graduate fellowship, a Facebook fellowship, the C.V. Ramamoorthy Distinguished Research Award, and the MIT Technology Review 35 under 35 Award, 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.

Website: https://ai.stanford.edu/~cbfinn

Academic Appointments

Honors & Awards

  • ACM Doctoral Dissertation Award, ACM (2019)
  • TR 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

Additional Info

  • Mail Code: 9020

Links

2021-22 Courses

Stanford Advisees

All Publications

  • 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., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

    View details for Web of Science ID 000683104605062

  • Learning Generalizable Robotic Reward Functions from "In-The-Wild" Human Videos Chen, A. S., Nair, S., Finn, C., 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., 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., 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., 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., Meila, M., Zhang, T. JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2021

    View details for Web of Science ID 000683104601047

  • Meta-Inverse Reinforcement Learning with Probabilistic Context Variables Yu, L., Yu, T., Finn, C., Ermon, S., 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., 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., 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