Mitsuhiko Nakamoto

Hi, I'm Mitsuhiko! I am a third-year CS Ph.D. student in the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley, advised by Professor Sergey Levine.

My research goal is to develop algorithms that empower robots with both high-level dexterity and generalization capabilities, and to incorporate them into everyday life. To this end, my current interests focus on data-driven approaches to solving real-world robotic tasks, including topics such as offline reinforcement learning (RL), online RL fine-tuning, and imitation learning.

Prior to Berkeley, I received my bachelor's degree from University of Tokyo in March 2022, advised by Professor Yoshimasa Tsuruoka. After graduating, I also had a great opportunity to spend time in Professor Yutaka Matsuo's Lab before moving to Berkeley. I've also worked on applications of deep learning to cardiovascular medicine at the AI Lab of the University of Tokyo Hospital.

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News
Research

* denotes equal contribution
Conference Papers and Pre-prints
Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance
Mitsuhiko Nakamoto, Oier Mees, Aviral Kumar, Sergey Levine.
CoRL 2024
arxiv link / project page / video / code
We propose V-GPS (Value-Guided Policy Steering), a general and broadly applicable approach that enhances the performance of pre-trained generalist robot policies at deployment time by re-ranking their actions according to a value function learned via offline RL.
SuSIE: Zero-Shot Robotic Manipulation with Pretrained Image-Editing Diffusion Models
Kevin Black*, Mitsuhiko Nakamoto*, Pranav Atreya, Homer Walke, Chelsea Finn, Aviral Kumar, Sergey Levine.
ICLR 2024
arxiv link / project page / code
We propose SuSIE (Subgoal Synthesis via Image Editing), a method that leverages text-to-image diffusion models (Stable Diffusion, InstructPix2Pix) for zero-shot robot planning.
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
Mitsuhiko Nakamoto*, Yuexiang Zhai*, Anikait Singh, Max Sobol Mark, Yi Ma, Chelsea Finn, Aviral Kumar, Sergey Levine.
NeurIPS 2023
arxiv link / project page / video
We propose calibrated Q-learning (Cal-QL), a method for acquiring an offline initialization that facilitates online fine-tuning.
Pre-Training for Robots: Offline RL Enables Learning New Tasks from a Handful of Trials
Aviral Kumar*, Anikait Singh*, Frederik Ebert*, Mitsuhiko Nakamoto, Yanlai Yang, Chelsea Finn, Sergey Levine
Robotics: Science and Systems (RSS) 2023
arxiv link / project page


Journal Papers
Deep Learning Algorithm to Detect Cardiac Sarcoidosis From Echocardiographic Movies
S.Katsushika, S.Kodera, M.Nakamoto, K.Ninomiya, N.Kakuda, H.Shinohara, R.Matsuoka, H.Ieki, M.Uehara, Y.Higashikuni, K.Nakanishi, T.Nakao, N.Takeda, K.Fujiu, M.Daimon, J.Ando, H.Akazawa, H.Morita, I.Komuro.
In Circulation Journal, 2021
paper link
Deep learning model to detect significant aortic regurgitation using electrocardiography
S.Sawano, S.Kodera, S.Katsushika, M.Nakamoto, K.Ninomiya, H.Shinohara, Y.Higashikuni, K.Nakanishi, T.Nakao, T.Seki, N.Takeda, K.Fujiu, M.Daimon, H.Akazawa, H.Morita, I.Komuro.
In Journal of Cardiology, 2021
paper link
Automatic detection of vessel structure by deep learning using intravascular ultrasound images of the coronary arteries
H.Shinohara, S.Kodera, K.Ninomiya, M.Nakamoto, KS.Katsushika, A.Saito, S.Minatsuki, H.Kikuchi, A.Kiyosue, Y.Higashikuni, N.Takeda, K.Fujiu, J.Ando, H.Akazawa, H.Morita, I.Komuro.
In PLOS ONE, 2021
paper link
Side Projects
Hadware Applications
Teleoperated Robot
  • This is a project done in this course
  • Designed the gripper and master interface for a teleoperated robot.
  • Technologies: CoppeliaSim, Arduino, Servo Motor, 3D CAD design, 3D Printer, potentiometers
  • [Click here to see demo videos]
Remote Switch Toggler
  • A device for turning on/off the electric switch remotely using a smartphone.
  • Technologies: ESP32, Servo Motor, BLE socket programming, electronic design (EAGLE)
  • [Click here to see demo videos]
Software Applications
Flappy Chameleon (a smartphone game app)
  • [App Store Link]
  • 2017.10-2018.2
  • Planned, designed, implemented by myself.
  • Technologies: C#, Unity
Foolip (a smartphone app for managing gourmet information)
  • App Store: (now unavailable)
  • [A review blog in Japanese]
  • 2018.6-2018.12
  • Planned, designed, implemented by a team of 3 members, mainly responsible for the front-end.
  • Technologies: React Native, Ruby on Rails
Misc
I am from Yokohama, Japan. My name in Japanese is 中本光彦.
Services
    Reviews: NeurIPS (2024), ICLR (2024, 2025), ICML (2025), ICRA (2025), RA-L
Hobbies
  • Soccer
  • Japanese Chess (Shogi)

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