KADOKAWA Yuki
Updated:
Affiliation
Autonomous, Intelligent, and Swarm Control Research Unit （Robotics）
Position
Researcher
Final Education
Completed the Doctoral Program in the Division of Information Science, Graduate School of Science and Technology, Nara Institute of Science and Technology
Degree(s)
Ph.D.（Engineering）
Research Keywords
Reinforcement Learning, Sim-to-Real Policy Transfer, Robot Control, Manipulation, Mobile Robots
Research Area
Robot Learning, Intelligent Robotics, Field Robotics
Academic Affiliations
- IEEE
- The Robotics Society of Japan
No information.
Peer-Reviewed Papers
- Yuki Kadokawa, Jonas Frey, Takahiro Miki, Takamitsu Matsubara, and Marco Hutter, "DAPPER: Discriminability-Aware Policy-to-Policy Preference-Based Reinforcement Learning for Query-Efficient Robot Skill Acquisition", IEEE Robotics & Automation Magazine (RAM), 2026
- Yuki Kadokawa, Hirotaka Tahara, and Takamitsu Matsubara, "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
- Yuki Kadokawa, Lingwei Zhu, Yoshihisa Tsurumine, and Takamitsu Matsubara, "Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain Randomization", Robotics and Autonomous Systems (RAS), 2023
Lectures / Oral Presentations, etc.
Ryo Watanabe, Takahiro Miki, Fan Shi, Yuki Kadokawa, Filip Bjelonic, Kento Kawaharazuka, Andrei Cramariuc, and Marco Hutter, "Learning Quiet Walking for a Small Home Robot", International Conference on Robotics and Automation (ICRA), 2025
Yuki Kadokawa, Masashi Hamaya, and Kazutoshi Tanaka, "Learning Robotic Powder Weighing from Simulation for Laboratory Automation", IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023
Yuki Kadokawa, Yoshihisa Tsurumine, and Takamitsu Matsubara, "Binarized P-Network: Deep Reinforcement Learning of Robot Control from Raw Images on FPGA", IEEE International Conference on Robotics and Automation (ICRA), 2023
Awards
- IEEE Kansai Section, Student Paper Award, 2022
No information.
No information.