World's Best Scientists 2026 revealed!
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Rising Stars
2025

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Rising Stars

D-Index
74
Citations
45175
World Ranking
53
National Ranking
6

Computer Science

D-Index
75
Citations
35763
World Ranking
1375
National Ranking
717

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Chelsea Finn is affiliated with Stanford University in the United States. Their research primarily belongs to the field of Computer Science, with a notable focus on Artificial Intelligence. Other subfields that inform their work include Computer Vision and Pattern Recognition, Control and Systems Engineering, Management Science and Operations Research, and Mechanical Engineering.

The research topics covered by Chelsea Finn emphasize applications in robotics and machine learning. Key areas of their work include:

  • Reinforcement Learning in Robotics
  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Robot Manipulation and Learning
  • Topic Modeling
  • Machine Learning and Data Classification
  • Human Pose and Action Recognition

Chelsea Finn has a substantial publication record with frequent contributions to several venues, notably:

  • arXiv (Cornell University)
  • The International Journal of Robotics Research
  • IEEE Robotics and Automation Letters
  • Springer proceedings in advanced robotics
  • 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Recent papers by Chelsea Finn include:

  • "On the Opportunities and Risks of Foundation Models" (2021, arXiv (Cornell University))
  • "Do As I Can, Not As I Say: Grounding Language in Robotic Affordances" (2022, arXiv (Cornell University))
  • "How to train your robot with deep reinforcement learning: lessons we have learned" (2021, The International Journal of Robotics Research)
  • "WILDS: A Benchmark of in-the-Wild Distribution Shifts" (2020, The Caltech Institute Archives (California Institute of Technology))
  • "Direct Preference Optimization: Your Language Model is Secretly a Reward Model" (2023, arXiv (Cornell University))

Collaborations are a significant aspect of their research, with frequent coauthors including:

  • Sergey Levine
  • Rafael Rafailov
  • Karol Hausman
  • Archit Sharma
  • Eric Mitchell

Best Publications

  • Model-agnostic meta-learning for fast adaptation of deep networks

    Chelsea Finn;Pieter Abbeel;Sergey Levine

  • End-to-end training of deep visuomotor policies

    Sergey Levine;Chelsea Finn;Trevor Darrell;Pieter Abbeel

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • Unsupervised Learning for Physical Interaction through Video Prediction

    Chelsea Finn;Ian J. Goodfellow;Sergey Levine

  • Guided cost learning: deep inverse optimal control via policy optimization

    Chelsea Finn;Sergey Levine;Pieter Abbeel

  • Deep visual foresight for planning robot motion

    Chelsea Finn;Sergey Levine

  • Model-Based Reinforcement Learning for Atari

    Lukasz Kaiser;Mohammad Babaeizadeh;Piotr Milos;Blazej Osinski

  • How to train your robot with deep reinforcement learning: lessons we have learned:

    Julian Ibarz;Jie Tan;Chelsea Finn;Chelsea Finn;Mrinal Kalakrishnan

  • Gradient Surgery for Multi-Task Learning

    Tianhe Yu;Saurabh Kumar;Abhishek Gupta;Sergey Levine

  • Probabilistic Model-Agnostic Meta-Learning

    Chelsea Finn;Kelvin Xu;Sergey Levine

  • Deep spatial autoencoders for visuomotor learning

    Chelsea Finn;Xin Yu Tan;Yan Duan;Trevor Darrell

  • Direct Preference Optimization: Your Language Model is Secretly a Reward Model

    Unknown

  • Meta-Learning with Implicit Gradients

    Aravind Rajeswaran;Chelsea Finn;Sham M. Kakade;Sergey Levine

  • RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

    Unknown

  • Stochastic Variational Video Prediction

    Mohammad Babaeizadeh;Chelsea Finn;Dumitru Erhan;Roy H. Campbell

  • Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning

    Anusha Nagabandi;Ignasi Clavera;Simin Liu;Ronald S. Fearing

  • One-Shot Visual Imitation Learning via Meta-Learning

    Chelsea Finn;Tianhe Yu;Tianhao Zhang;Pieter Abbeel

  • Stochastic Adversarial Video Prediction

    Alex X. Lee;Richard Zhang;Frederik Ebert;Pieter Abbeel

  • Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

    Kate Rakelly;Aurick Zhou;Deirdre Quillen;Chelsea Finn

  • Model Based Reinforcement Learning for Atari

    Łukasz Kaiser;Mohammad Babaeizadeh;Piotr Miłos;Błażej Osiński

  • Recasting gradient-based meta-learning as hierarchical bayes

    Erin Grant;Chelsea Finn;Sergey Levine;Trevor Darrell

  • Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning

    Tianhe Yu;Deirdre Quillen;Zhanpeng He;Ryan Julian

  • MOPO: Model-based Offline Policy Optimization

    Tianhe Yu;Garrett Thomas;Lantao Yu;Stefano Ermon

Frequent Co-Authors

Sergey Levine
Sergey Levine University of California, Berkeley
Pieter Abbeel
Pieter Abbeel University of California, Berkeley
Trevor Darrell
Trevor Darrell University of California, Berkeley
Dumitru Erhan
Dumitru Erhan Google (United States)
George Tucker
George Tucker Google (United States)
Roy H. Campbell
Roy H. Campbell University of Illinois at Urbana-Champaign
Jiajun Wu
Jiajun Wu Stanford University
Stefano Ermon
Stefano Ermon Stanford University
Sham M. Kakade
Sham M. Kakade Harvard University

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