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 3rd 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 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.
Naoki Yokoyama, Qian Luo, Dhruv Batra, Sehoon Ha
International Conference on Intelligent Robots and Systems (IROS) 2022
Embodied AI Workshop at Conference on Computer Vision and Pattern Recognition (CVPR) 2022
1st place in Interactive Navigation, 5th in Social Navigation.
Ruslan Partsey, Erik Wijmans, Naoki Yokoyama, Oles Dobosevych, Dhruv Batra, Oleksandr Maksymets
Conference on Computer Vision and Pattern Recognition (CVPR) 2022
Can an autonomous agent navigate in a new environment without ever building an explicit map?
Naoki Yokoyama, Sehoon Ha, Dhruv Batra
International Conference on Intelligent Robots and Systems (IROS) 2021
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.