Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain Randomization Y Kadokawa, L Zhu, Y Tsurumine, T Matsubara Robotics and Autonomous Systems 165, 104425, 2023 | 14 | 2023 |
Binarized P-Network: Deep Reinforcement Learning of Robot Control from Raw Images on FPGA Y Kadokawa, Y Tsurumine, T Matsubara IEEE Robotics and Automation Letters 6 (4), 8545-8552, 2021 | 6 | 2021 |
Learning robotic powder weighing from simulation for laboratory automation Y Kadokawa, M Hamaya, K Tanaka 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2023 | 5 | 2023 |
Robust Iterative Value Conversion: Deep Reinforcement Learning for Neurochip-Driven Edge Robots Y Kadokawa, T Kodera, Y Tsurumine, S Nishimura, T Matsubara Robotics and Autonomous Systems, 104782, 2024 | 1 | 2024 |
Learning Quiet Walking for a Small Home Robot R Watanabe, T Miki, F Shi, Y Kadokawa, F Bjelonic, K Kawaharazuka, ... arXiv preprint arXiv:2502.10983, 2025 | | 2025 |
Progressive-Resolution Policy Distillation: Leveraging Coarse-Resolution Simulation for Time-Efficient Fine-Resolution Policy Learning Y Kadokawa, H Tahara, T Matsubara arXiv preprint arXiv:2412.07477, 2024 | | 2024 |
Edge-server Deep Reinforcement Learning of Quantized Policy for Neurochip Implimentation T KODERA, Y KADOKAWA, Y TSURUMINE, SYA NISHIMURA, ... 計測自動制御学会制御部門マルチシンポジウム (CD-ROM) 10, 2-6, 2023 | | 2023 |
FPGA を用いた実時間ロボット制御のための深層強化学習アルゴリズムの開発 角川勇貴, カドカワユウキ 奈良先端科学技術大学院大学, 2021 | | 2021 |
FPGA を用いた実時間ロボット制御のための深層強化学習手法 Binary P-Network の提案 Y KADOKAWA, Y TSURUMINE, T MATSUBARA 日本ロボット学会学術講演会予稿集 (CD-ROM) 38, 3-4, 2020 | | 2020 |
講演 設計コンテスト 2017 への参加 (富山県立大学チームの事例) 角川勇貴, 中根和城, 神谷和秀 設計工学= Journal of Japan Society for Design Engineering: 日本設計工学会誌 …, 2019 | | 2019 |