Yuki Kadokawa
Sim-to-Real Reinforcement Learning for Robotics
I am a researcher in reinforcement learning (RL), deep learning, and robotics. My research focuses on Sim-to-Real transfer — how robots can learn in simulation and operate in the real world — using methods such as domain randomization, policy distillation, and sample-efficient reinforcement learning. I have worked on real-world robotic tasks including manipulation, legged locomotion, multi-robot systems, and field and industrial robotics.
Cross-appointment
- Researcher, Fukushima Institute for Research, Education and Innovation (F-REI)
- Project Assistant Professor, Nara Institute of Science and Technology (NAIST) — Robot Learning Lab
News & Highlights
2026
NEWAppointed as an Associate Editor for
ICRA 2027 (IEEE ICRA Conference Editorial Board), to be held in Seoul,
Korea.
2026
Gave an invited talk at the SICE Symposium
on Decentralized Autonomous Systems (自律分散システム・シンポジウム) on Sim-to-Real RL for real-world
robot tasks.
2026
DAPPER was accepted to IEEE Robotics & Automation
Magazine (RAM) and presented at ICRA 2026 — joint work with ETH Zürich.
2026
Our paper on Distilled Iterative Value Conversion for
neurochip-driven edge robots was published in IEEE Access.
2025
Progressive-Resolution Policy Distillation (PRPD) was
published in IEEE T-ASE and featured in the Nikkan Kogyo Shimbun.
2025
Learning Quiet Walking for a Small Home Robot was
presented at ICRA 2025 — collaboration with ETH Zürich and Sony.
2024
Completed my Doctor of Engineering at NAIST and began as a Project
Assistant Professor in the Robot Learning Lab.
Publications
31International Journal
Prolonging Tool Life: Learning Skillful Use of General-purpose Tools
through Lifespan-guided Reinforcement Learning
IEEE Access, 2026
Progressive-Resolution Policy Distillation: Leveraging
Coarse-Resolution Simulations for Time-Efficient Fine-Resolution Policy Learning
IEEE Transactions on Automation Science and Engineering (T-ASE), 2025
ViSA: Visited-State Augmentation for Generalized Goal-Space Contrastive
Reinforcement Learning
Under review
Bridged SBI: Correcting Biased Low-Fidelity Posteriors for
Cost-Efficient High-Fidelity Inference
Under review
International Conference
ICRA2026
ICRA2026
Progressive-Resolution Policy Distillation: Leveraging
Coarse-Resolution Simulations for Time-Efficient Fine-Resolution Policy Learning
IEEE International Conference on Robotics and Automation (ICRA, T-ASE option), 2026
Learning Quiet Walking for a Small Home Robot
International Conference on Robotics and Automation (ICRA), 2025
AROB2025
Scalable Domain Randomized Reinforcement Learning for Sim-to-Real
Policy Transfer in Complex Robot Tasks
International Symposium on Artificial Life and Robotics (AROB), 2025
Learning Robotic Powder Weighing from Simulation for Laboratory
Automation
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023
ICRA2023
Domestic Conference
RSJ2026
RSJ2026
Uncertainty-Guided Active Aerial Survey via Post-Disaster Orthophoto
Diffusion Inpainting
Annual Conference of the Robotics Society of Japan, 2026
RSJ2026
ROBOMECH2026
SICE SI2025
RSJ2025
RSJ2025
RSJ2025
RSJ2024
Deep Reinforcement Learning with FPNN-to-SNN Policy Distillation for
Neurochip-driven RobotsBest
Presentation Award Finalist
Annual Conference of the Robotics Society of Japan, 2024
ROBOMECH2024
SICE MSCS2023
RSJ2020
Invited Talk
SICE DAS2026
実世界ロボットタスクにおけるSim-to-Real強化学習: パーティクルシミュレーションにおける計算量と精度のトレードオフ
計測自動制御学会 自律分散システム・シンポジウム, 2026
Honors
Awards
- Outstanding Student Award, Symposium of University Fellowship, NAIST, 2022 [Award Page]
- Student Paper Award, IEEE Kansai Section, 2022 [NAIST] [IEEE]
- Best Student of the Year, Toyama Prefectural University, 2019
- Grand Prize, Monozukuri in Toyama, Toyama Mechanical and Electrical Industries Association, 2018
- Best Presentation Award, Design Competition, Japan Society for Design Engineering, 2018
- 3rd Place (National Finals 3/10), Design Competition, Japan Society for Design Engineering, 2018
- Best Student Presentation Award, Research Presentation, Japan Society for Design Engineering, 2018
- Best Presentation Award, Design Competition, Japan Society for Design Engineering, 2017
- Best Student of the Year, Toyama Prefectural Takaoka Kogei High School, 2015
Scholarship & Grant
- Senju Monju Project (Research Grant), NAIST, 2024–2026
- Scholarship (Return Exemption, Full Amount), Japan Student Services Organization, 2023
- Research Fellowship for Young Scientists: DC2 (Funding & Salary), JSPS, 2023–2025
- Research Fellowship (Research Grant & Salary), Japan Science and Technology Agency, 2022–2023 [Interview]
- Scholarship (Return Exemption, Half Amount), Japan Student Services Organization, 2021
- Outstanding Student Scholarship (Tuition Exemption), NAIST, 2021
Service
Editorial Board
- Associate Editor, Conference Editorial Board (CEB), IEEE International Conference on Robotics and Automation (ICRA 2027), Seoul, Korea [ICRA 2027]
Experience & Education
Work History
-
Apr 2024 – Mar 2026Project Assistant ProfessorRobot Learning Lab, NAIST
-
Aug 2023 – Oct 2023Visiting ResearcherRobotic Systems Lab, ETH Zürich
-
Apr 2022 – Mar 2023Research InternshipOMRON SINIC X Corporation
-
Dec 2018 – Mar 2019Part-time, 3D-CAD ModelingAluminum Factory Corporation
Education
-
2024Doctor of EngineeringProgram of Information Science and Engineering, NAIST, Japan
-
2021Master of EngineeringProgram of Information Science and Engineering, NAIST, Japan
-
2019Bachelor of EngineeringDepartment of Intelligent Robotics, Toyama Prefectural University, Japan
Research Field
Reinforcement Learning
Deep Learning
Robotics
Sim-to-Real
Domain Randomization
Edge Robots
FPGA
Neurochip
Contact
E-mail
kadokawa.yuki [at] naist.ac.jp
Address
8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
Affiliation
Nara Institute of Science and Technology, Robot Learning Laboratory