| 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 | 136 | 2021 |
| Open x-embodiment: Robotic learning datasets and rt-x models A Padalkar, A Pooley, A Jain, A Bewley, A Herzog, A Irpan, A Khazatsky, ... arXiv preprint arXiv:2310.08864, 2023 | 92* | 2023 |
| 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 | 18 | 2021 |
| 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 | 13* | 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 | 11 | 2022 |
| Tool as embodiment for recursive manipulation Y Noguchi, T Matsushima, Y Matsuo, SS Gu arXiv preprint arXiv:2112.00359, 2021 | 7 | 2021 |
| Real-World Robot Applications of Foundation Models: A Review K Kawaharazuka, T Matsushima, A Gambardella, J Guo, C Paxton, ... arXiv preprint arXiv:2402.05741, 2024 | 6 | 2024 |
| 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 | 4 | 2023 |
| 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, ... arXiv preprint arXiv:2309.14425, 2023 | 2 | 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 |
| 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 |
| Generalizable One-shot Rope Manipulation with Parameter-Aware Policy S Kuroki, J Guo, T Matsushima, T Okubo, M Kobayashi, Y Ikeda, ... arXiv preprint arXiv:2306.09872, 2023 | 1 | 2023 |
| 学生フォラム [第 108 回] 学生フォ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 |
| 学生フォラム [第 107 回] 大澤正彦先生インタビュ 「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ムワク 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 |
| サビスロボットシステムにおけるデタドリブンな開発工程の検討—World Robot Summit 2020 Partner Robot Challenge での事例を踏まえた考察— 松嶋達也， 野口裕貴， 有馬純平， 原田憲旺， 青木俊樹， 沖田祐樹， ... 日本ロボット学会誌 42 (2), 189-192, 2024 | | 2024 |
| GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy S Kuroki, J Guo, T Matsushima, T Okubo, M Kobayashi, Y Ikeda, ... arXiv preprint arXiv:2309.09051, 2023 | | 2023 |
| M3IL: Multi-Modal Meta-Imitation Learning X Zhang, T Matsushima, Y Matsuo, Y Iwasawa Transactions of the Japanese Society for Artificial Intelligence 38 (2), A-LB3, 2023 | | 2023 |
| 世界モデルを用いた画像·深度·触覚のマルチモダル学習 上條達也， 石本幸暉， 松嶋達也， 岩澤有祐， 松尾豊 人工知能学会全国大会論文集 第 37 回 (2023), 2G1OS21c01-2G1OS21c01, 2023 | | 2023 |
Shixiang Shane GuGoogle DeepMind在 google.com 的电子邮件经过验证
