PeopleofSuperintelligence

Researcher

Fei Xia

Fei Xia is a robotics researcher whose work spans simulation for embodied agents and language-guided robot control. He earned a bachelor's degree at Tsinghua in 2016 and a Stanford electrical-engineering PhD in 2021, coauthored Gibson Env and iGibson, and announced plans to join Robotics at Google. His work includes SayCan, RT-1, PaLM-E, RT-2 and Open X-Embodiment; AdaFDR won the RECOMB Best Paper Award in 2019, and he coauthored a 2024 study of physically grounded vision-language models. As of October 2026, he is a Member of Technical Staff at Anthropic, after research roles at Meta and Google DeepMind.

Timeline

  1. 2016–2021

    Gibson Env and iGibson

    He earns a bachelor's degree at Tsinghua in 2016 and begins a PhD in electrical engineering at Stanford. In 2018, he coauthors Gibson Env, a simulation that virtualizes real spaces for embodied agents; in 2019, AdaFDR wins the RECOMB Best Paper Award, and iGibson 1.0 launches in December 2020 with 15 interactive home-sized scenes and 108 rooms. In March 2021, he announces plans to join Robotics at Google as a research scientist that fall, and in May 2021 he defends his thesis on large-scale simulation for embodied perception and robot learning.

  2. 2022

    SayCan and RT-1

    SayCan grounds language-model instructions in feasible robot skills, and he coauthors the 2022 RT-1 paper, a Robotics Transformer for real-world control.

  3. 2023

    PaLM-E, RT-2 and Open X

    He coauthors PaLM-E, which incorporates visual and continuous sensor inputs into a language model. RT-2 combines a pretrained vision-language model with robot data for action, while Open X-Embodiment pools data from 22 robot types.

  4. 2024

    Physically grounded vision-language models

    He coauthors the ICRA 2024 paper “Physically Grounded Vision-Language Models for Robotic Manipulation,” which introduces PhysObjects, an object-centric dataset of 39.6K crowd-sourced and 417K automated physical-concept annotations of household objects.

  5. As of October 2026

    Member of Technical Staff

    He is a Member of Technical Staff at Anthropic after working as an AI Research Scientist at Meta and a Senior Staff Research Scientist and Tech Lead Manager on Google DeepMind's Robotics team.

  1. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI from 1961 to 2025, with a year-by-year chart.
  2. The United States employs an estimated 53% of people in our data with a known employment countryOur estimates of where AI careers are concentrated now and how countries’ shares of dated positions changed over time.
  3. 46% of living people in our data with known birth and employment countries work outside their birth countryOur estimate of cross-border careers in AI, from country of birth and the country of each current organization.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionOur estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.

Sources and credits

Photo: We have no published photo of Fei Xia yet. Send us a free-licensed photo.

  1. Fei Xia's Homepage - Member of Technical Staff at Anthropic fxia22.github.io. Checked 19 October 2026.
  2. [1808.10654] Gibson Env: Real-World Perception for Embodied Agents arxiv.org. Checked 19 October 2026.
  3. AWARDS – RECOMB 2019 recomb.org. Checked 19 October 2026.
  4. [2012.02924] iGibson 1.0: a Simulation Environment for Interactive Tasks in Large Realistic Scenes arxiv.org. Checked 19 October 2026.
  5. SayCan: Grounding Language in Robotic Affordances say-can.github.io. Checked 19 October 2026.
  6. [2212.06817] RT-1: Robotics Transformer for Real-World Control at Scale arxiv.org. Checked 19 October 2026.
  7. [2303.03378] PaLM-E: An Embodied Multimodal Language Model arxiv.org. Checked 19 October 2026.
  8. RT-2: New model translates vision and language into action — Google DeepMind deepmind.google. Checked 19 October 2026.
  9. Scaling up learning across many different robot types — Google DeepMind deepmind.google. Checked 19 October 2026.
  10. Physically Grounded VLMs iliad.stanford.edu. Checked 19 October 2026.