Ph.D. Student in Robotics at Georgia Tech
B.S. and M.S. Electrical Engineering
Concn. in Machine Learning & Computer Vision
I am currently a 4th year Robotics Ph.D. student at Georgia Tech advised by Dhruv Batra and Sehoon Ha. Previously, I graduated with my BS and MS from Northeastern University. My research interests involve scalable learning methods that will teach robots to effectively perceive and interact within various environments in the real world by training them within realistic simulators before transferring the learned skills to reality.
During my PhD, I've interned at the Boston Dynamics AI Institute with Bernadette Bucher and Jiuguang Wang (Summer 2023), at Amazon with Gaurav Sukhatme on deep reinforcement learning for robotics with reward decomposition (Summer 2022), and at Meta AI with Akshara Rai on mobile manipulation for object rearrangement (Summer 2021).
Previously I also worked with Taskin Padir in the Robotics and Intelligent Vehicles Research (RIVeR) lab at Northeastern University. There, I led Team Northeastern in mutiple international robotics competitions such as the 2019 RoboCup@Home competition in Sydney, Australia, the 2018 World Robot Summit in Tokyo, Japan, and the Robocup@Home 2018 in Montreal, Canada, where we placed 4th internationally and 1st in the USA.
I have also had the pleasure of mentoring other students, such as Qian Luo (MS@GT), Simar Kareer (MS@GT), and Marco Delgado (BS@GT) in research projects.
Tsung-Yen Yang, Sergio Arnaud, Kavit Shah, Naoki Yokoyama, Alexander Clegg, Joanne Truong , Eric Undersander, Oleksandr Maksymets, Sehoon Ha, Mrinal Kalakrishnan, Roozbeh Mottaghi, Dhruv Batra, Akshara Rai
CVPR 2023 Demo Track
CVPR 2023 Meta AI Booth
Karmesh Yadav*, Arjun Majumdar*, Ram Ramrakhya, Naoki Yokoyama, Aleksei Baevski, Zsolt Kira, Oleksandr Makysmets, Dhruv Batra
Naoki Yokoyama, Qian Luo, Dhruv Batra, Sehoon Ha
Embodied AI Workshop at CVPR 2022
1st place in Interactive Navigation, 5th in Social Navigation.
Naoki Yokoyama, Sehoon Ha, Dhruv Batra
Dynamics-aware training and evaluation for navigation. Demonstrated that trained agents better leveraged the dynamics of the robot to be faster than previous work, both within simulation and in the real world.
Finished 1st place among US teams.
Competition with mobile manipulation and perception tasks, held in Odaiba's Tokyo Big Sight.