Chelsea Finn

About

Chelsea Finn is a pioneering AI and robotics researcher whose work sits at the intersection of machine learning, robot learning, and foundation models. Best known for her foundational contributions to meta-learning and end-to-end robot control, Finn has helped reshape how machines acquire and generalize skills from limited data. Her 2016 work on deep visuomotor policies — now cited over 3,000 times across versions — demonstrated that robots could learn complex manipulation behaviors directly from raw visual input without hand-engineered components, a landmark result in end-to-end robot learning. Her research on deep visual foresight and guided cost learning further advanced model-based reinforcement learning and inverse optimal control, enabling robots to plan and imitate behavior more efficiently. More recently, Finn has contributed to the frontier of large-scale robot learning, with influential work on Robotics Transformer (RT-1) and the ALOHA bimanual manipulation system, as well as the widely-read Foundation Models report (2,177 citations) that helped define the modern paradigm of adaptable, broadly-trained AI systems. Across her career, her research has accumulated tens of thousands of citations, cementing her reputation as one of the most impactful voices in contemporary AI and embodied intelligence.

Research Focus

Key Achievements

Top Papers

1. 1
   On the Opportunities and Risks of Foundation Models

   2,177 citations · 2021
2. 2
   End-to-end training of deep visuomotor policies

   1,715 citations · 2016
3. 3
   End-to-End Training of Deep Visuomotor Policies

   1,399 citations · 2015
4. 4
   Deep visual foresight for planning robot motion

   627 citations · 2017
5. 5
   How to train your robot with deep reinforcement learning: lessons we have learned

   536 citations · 2021
6. 6
   Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

   516 citations · 2022
7. 7
   RT-1: Robotics Transformer for Real-World Control at Scale

   512 citations · 2023
8. 8
   Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware

   438 citations · 2023
9. 9
   Guided Cost Learning: Deep Inverse Optimal Control via Policy Optimization

   370 citations · 2016
10. 10
    Guided cost learning: deep inverse optimal control via policy optimization

    333 citations · 2016

Key Collaborators

Contact & Links