Anthony Brohan

About

Anthony Brohan is a leading researcher at the intersection of robotics, natural language processing, and large-scale machine learning, whose work is fundamentally reshaping how robots understand and execute human instructions. Brohan’s primary contributions lie in grounding high-level language commands into physical robotic actions, moving beyond simple scripted behaviors to enable true semantic reasoning. He is the driving force behind the groundbreaking Robotics Transformer (RT) series, including RT-1 (512 citations) and RT-2 (267 citations), which pioneered the use of large vision-language models for end-to-end robotic control. His seminal paper, “Do As I Can, Not As I Say” (516 citations), introduced the concept of using large language models to infer affordances, allowing robots to act on what is physically possible rather than just following literal text. Brohan also spearheaded the Open X-Embodiment collaboration (over 200 combined citations), creating the largest open-source dataset for cross-embodiment robot learning. Most recently, his work on Gemini Robotics (2025) aims to bring generalist AI models into the physical world. With over 1,600 total citations, Brohan’s research is not only highly cited but is also defining the next generation of capable, language-guided robots.

Research Focus

Key Achievements

Top Papers

1. 1
   Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

   516 citations · 2022
2. 2
   RT-1: Robotics Transformer for Real-World Control at Scale

   512 citations · 2023
3. 3
   RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control

   267 citations · 2023
4. 4
   Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration⁰

   119 citations · 2024
5. 5
   Open X-Embodiment: Robotic Learning Datasets and RT-X Models

   101 citations · 2023
6. 6
   Scaling Robot Learning with Semantically Imagined Experience

   66 citations · 2023
7. 7
   RT-1: Robotics Transformer for Real-World Control at Scale

   38 citations · 2022
8. 8
   Robotic Skill Acquisition via Instruction Augmentation with Vision-Language Models

   29 citations · 2023
9. 9
   Scaling Robot Learning with Semantically Imagined Experience

   4 citations · 2023
10. 10
    Gemini Robotics: Bringing AI into the Physical World

    4 citations · 2025

Key Collaborators

Contact & Links