| Open X-Embodiment: Robotic Learning Datasets and RT-X Models: Open X-Embodiment Collaboration A O’Neill, A Rehman, A Maddukuri, A Gupta, A Padalkar, A Lee, A Pooley, ... 2024 IEEE International Conference on Robotics and Automation (ICRA), 6892-6903, 2024 | 545* | 2024 |
| Deployment-efficient reinforcement learning via model-based offline optimization T Matsushima, H Furuta, Y Matsuo, O Nachum, S Gu International Conference on Learning Representations, 2021 | 172 | 2021 |
| Real-world robot applications of foundation models: A review K Kawaharazuka, T Matsushima, A Gambardella, J Guo, C Paxton, ... Advanced Robotics 38 (18), 1232-1254, 2024 | 45 | 2024 |
| Co-adaptation of algorithmic and implementational innovations in inference-based deep reinforcement learning H Furuta, T Kozuno, T Matsushima, Y Matsuo, SS Gu Advances in neural information processing systems 34, 9828-9842, 2021 | 21* | 2021 |
| Policy information capacity: Information-theoretic measure for task complexity in deep reinforcement learning H Furuta, T Matsushima, T Kozuno, Y Matsuo, S Levine, O Nachum, ... International Conference on Machine Learning, 3541-3552, 2021 | 21 | 2021 |
| World robot challenge 2020–partner robot: a data-driven approach for room tidying with mobile manipulator T Matsushima, Y Noguchi, J Arima, T Aoki, Y Okita, Y Ikeda, K Ishimoto, ... Advanced Robotics 36 (17-18), 850-869, 2022 | 15 | 2022 |
| Self-recovery prompting: Promptable general purpose service robot system with foundation models and self-recovery M Shirasaka, T Matsushima, S Tsunashima, Y Ikeda, A Horo, S Ikoma, ... 2024 IEEE International Conference on Robotics and Automation (ICRA), 17395 …, 2024 | 10 | 2024 |
| Tool as embodiment for recursive manipulation Y Noguchi, T Matsushima, Y Matsuo, SS Gu arXiv preprint arXiv:2112.00359, 2021 | 8 | 2021 |
| Neuron as an Agent S Ohsawa, K Akuzawa, T Matsushima, G Bezerra, Y Iwasawa, H Kajino, ... | 6 | 2018 |
| Collective intelligence for 2d push manipulations with mobile robots S Kuroki, T Matsushima, J Arima, H Furuta, Y Matsuo, SS Gu, Y Tang IEEE Robotics and Automation Letters 8 (5), 2820-2827, 2023 | 5 | 2023 |
| Modeling task uncertainty for safe meta-imitation learning T Matsushima, N Kondo, Y Iwasawa, K Nasuno, Y Matsuo Frontiers in Robotics and AI 7, 606361, 2020 | 2 | 2020 |
| GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy S Kuroki, J Guo, T Matsushima, T Okubo, M Kobayashi, Y Ikeda, ... 2024 IEEE International Conference on Robotics and Automation (ICRA), 14792 …, 2024 | 1 | 2024 |
| TRAIL team description paper for RoboCup@ Home 2023 C Tsuji, D Komukai, M Shirasaka, H Wada, T Omija, A Horo, D Furuta, ... arXiv preprint arXiv:2310.03913, 2023 | 1 | 2023 |
| M3IL: Multi-Modal Meta-Imitation Learning X Zhang, T Matsushima, Y Matsuo, Y Iwasawa 人工知能学会論文誌 (Web) 38 (2), 1-10, 2023 | 1* | 2023 |
| 学生フォーラム [第 108 回] 学生フォーラムから探る若手研究者のキャリア形成 津村賢宏， 佐久間洋司， 西村優佑， 福島康太郎， 松嶋達也 人工知能 36 (6), 794-797, 2021 | 1 | 2021 |
| 学生フォーラム [第 107 回] 大澤正彦先生インタビュー 「ドラえもんから紐解く人工知能・ロボット」 津村賢宏， 松嶋達也， 宮本拓 人工知能 36 (5), 654-658, 2021 | 1 | 2021 |
| Pixyz: 複雑な深層生成モデル開発のためのフレームワーク 鈴木雅大， 金子貴輝， 谷口尚平， 松嶋達也， 松尾豊 人工知能学会全国大会論文集 第 33 回 (2019), 1L2J1105-1L2J1105, 2019 | 1 | 2019 |
| Special issue on real-world robot applications of the foundation models K Kawaharazuka, T Matsushima, S Kurita, C Paxton, A Zeng, T Ogata, ... Advanced Robotics 38 (18), 1231-1231, 2024 | | 2024 |
| Data-driven Development Process in Service Robot Systems-Discussion Based on a Case Study from the World Robot Summit 2020 Partner Robot Challenge T MATSUSHIMA, Y NOGUCHI, J ARIMA, KENO HARADA, T AOKI, ... 日本ロボット学会誌 42 (2), 189-192, 2024 | | 2024 |
| NLP2024 併設ワークショップ 「大規模言語モデルの実世界応用」 吉野幸一郎， 谷口忠大， 持橋大地， 河原塚健人， 松嶋達也， 品川政太朗， ... 自然言語処理 31 (2), 809-815, 2024 | | 2024 |
Shixiang Shane GuGoogle DeepMindایمیل تأیید شده در google.com