Jay Vakil

I am a first year PhD student in the Computer Science department at the University of Colorado Boulder advised by Alessandro Roncone and Nikolaus Correll. I am also mentored by Vikash Kumar and Chris Paxton. Previously, I was a researcher at the Facebook AI Research (FAIR) with the Embodied AI team.

I earned my Bachelor of Science in Electrical Engineering with a Computer Science and Math minor from the University of Washington.

I am always excited to learn through conversations—please feel free to reach out if you’d like to collaborate or chat!

jay dot last_name at colorado dot edu

Some recent highlights from our research:
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Towards Open-World Mobile Manipulation in Homes: Lessons from the Neurips 2023 HomeRobot Open Vocabulary Mobile Manipulation Challenge

Sriram Yenamandra*, Arun Ramachandran*, Mukul Khanna*, Karmesh Yadav*, Jay Vakil*, Andrew Melnik, Michael Büttner, Leon Harz, Lyon Brown, Gora Chand Nandi, Arjun PS, Gaurav Kumar Yadav, Rahul Kala, Robert Haschke, Yang Luo, Jinxin Zhu, Yansen Han, Bingyi Lu, Xuan Gu, Qinyuan Liu, Yaping Zhao, Qiting Ye, Chenxiao Dou, Yansong Chua, Volodymyr Kuzma, Vladyslav Humennyy, Ruslan Partsey, Jonathan Francis, Devendra Singh Chaplot, Gunjan Chhablani, Alexander Clegg, Theophile Gervet, Vidhi Jain, Ram Ramrakhya, Andrew Szot, Austin Wang, Tsung-Yen Yang, Aaron Edsinger, Charlie Kemp, Binit Shah, Zsolt Kira, Dhruv Batra, Roozbeh Mottaghi, Yonatan Bisk, Chris Paxton
Website/ Paper


Open X-Embodiment: Robotic Learning Datasets and RT-X Models

Open-X X-Embodiment Collaboration

  • Best Conference Paper Award, ICRA 2024
  • Best Student Paper Award (Finalist), ICRA 2024
  • Best Paper Award in Robot Manipulation (Finalist)
  • International Conference on Robotics and Automation (ICRA), 2024
  • CoRL 2023 Workshop Towards Generalist Robots (Oral Presentation)

    • Website/ Paper/ Dataset/ Code


    OK-Robot: What Really Matters in Integrating Open-Knowledge Models for Robotics

    Peiqi Liu*, Yaswanth Orru*, Jay Vakil, Chris Paxton, Nur Muhammad "Mahi" Shafiullah†, Lerrel Pinto†

  • RSS 2024

    • Website/ Paper/ Code


    RoboHive: A Unified Framework for Robot Learning

    Vikash Kumar, Rutav Shah, Gaoyue Zhou, Vincent Moens, Vittorio Caggiano, Jay Vakil, Abhishek Gupta, and Aravind Rajeswaran

  • NeurIPS 2023

    • Website/ Paper/ Dataset/ Code/ PyPI


    RoboAgent: Towards Sample Efficient Robot Manipulation with Semantic Augmentations and Action Chunking

    Homanga Bharadhwaj*, Jay Vakil*, Mohit Sharma*, Abhinav Gupta, Shubham Tulsiani, and Vikash Kumar.

  • ICRA 2024
  • 6th Robot Learning Workshop @ NeurIPS 2023 (Outstanding presentation award)
  • Out-of-Distribution Generalization in Robotics @ CoRL 2023

    • Website/ Paper/ Dataset/ Code


    What do we learn from a large-scale study of pre-trained visual representations in sim and real environments?

    Sneha Silwal*, Karmesh Yadav*, Tingfan Wu*, Jay Vakil*, Arjun Majumdar*, Sergio Arnaud*, Claire Chen, Vincent-Pierre Berges, Dhruv Batra, Aravind Rajeswaran, Mrinal Kalakrishnan, Franziska Meier, and Oleksandr Maksymets

  • ICRA 2024

    • Website/ Paper


    Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?

    Arjun Majumdar, Karmesh Yadav, Sergio Arnaud, Yecheng Jason Ma, Claire Chen, Sneha Silwal, Aryan Jain, Vincent-Pierre Berges, Tingfan Wu, Jay Vakil, Pieter Abbeel, Jitendra Malik, Dhruv Batra, Yixin Lin, Oleksandr Maksymets, Aravind Rajeswaran, Franziska Meier

  • NeurIPS 2023

    • Website/ Paper/ Dataset/ Code


    Spatial-Language Attention Policies for Efficient Robot Learning

    Priyam Parashar, Vidhi Jain, Xiaohan Zhang, Jay Vakil, Sam Powers, and Chris Paxton

  • CoRL 2023

    • Website/ Paper




    2025

    Reviewer, IEEE International Conference on Robotics and Automation (ICRA)

    2024

    Reviewer, Conference on Robot Learning (CoRL)

    2024

    Reviewer, IEEE International Conference on Robotics and Automation (ICRA)