Publications

Some of the publications of our laboratory are listed here.

The publications of the entire Matsuo Laboratory can be viewed in this page.

So Kuroki, Jiaxian Guo, Tatsuya Matsushima, Takuya Okubo, Masato Kobayashi, Yuya Ikeda, Ryosuke Takanami, Paul Yoo, Yutaka Matsuo, Yusuke Iwasawa (2023). GenDOM: Generalizable One-shot Deformable Object Manipulation with Parameter-Aware Policy.

Cite arXiv Website

So Kuroki, Jiaxian Guo, Tatsuya Matsushima, Takuya Okubo, Masato Kobayashi, Yuya Ikeda, Ryosuke Takanami, Paul Yoo, Yutaka Matsuo, Yusuke Iwasawa (2023). GenORM: Generalizable One-shot Rope Manipulation with Parameter-Aware Policy.

Cite arXiv

Mimo Shirasaka, Tatsuya Matsushima, Soshi Tsunashima, Yuya Ikeda, Aoi Horo, So Ikoma, Chikaha Tsuji, Hikaru Wada, Tsunekazu Omija, Dai Komukai, Yutaka Matsuo, Yusuke Iwasawa (2023). Self-Recovery Prompting: Promptable General Purpose Service Robot System with Foundation Models and Self-Recovery.

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So Kuroki, Tatsuya Matsushima, Jumpei Arima, Hiroki Furuta, Yutaka Matsuo, Shixiang Shane Gu, Yujin Tang (2023). Collective Intelligence for 2D Push Manipulations With Mobile Robots. IEEE Robotics and Automation Letters.

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Tatsuya Matsushima, Yuki Noguchi, Jumpei Arima, Toshiki Aoki, Yuki Okita, Yuya Ikeda, Koki Ishimoto, Shohei Taniguchi, Yuki Yamashita, Shoichi Seto, Shixiang Shane Gu, Yusuke Iwasawa, Yutaka Matsuo (2022). World robot challenge 2020 – partner robot: a data-driven approach for room tidying with mobile manipulator. Advanced Robotics.

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Hiroki Furuta, Tatsuya Matsushima, Tadashi Kozuno, Yutaka Matsuo, Sergey Levine, Ofir Nachum, Shixiang Shane Gu (2021). Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning. ICML 2021.

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Tatsuya Matsushima, Hiroki Furuta, Yutaka Matsuo, Ofir Nachum, Shixiang Shane Gu (2021). Deployment-Efficient Reinforcement Learning via Model-Based Offline Optimization. ICLR 2021.

PDF Cite Code Project Slides

Yuki Noguchi, Tatsuya Matsushima, Yutaka Matsuo, Shixiang Shane Gu (2021). Tool as Embodiment for Recursive Manipulation.

Cite arXiv

Hiroki Furuta, Tadashi Kozuno, Tatsuya Matsushima, Yutaka Matsuo, Shixiang Shane Gu (2021). Co-adaptation of algorithmic and implementational innovations in inference-based deep reinforcement learning. NeurIPS 2021.

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Tatsuya Matsushima, Naruya Kondo, Yusuke Iwasawa, Kaoru Nasuno, Yutaka Matsuo (2020). Modeling Task Uncertainty for Safe Meta-Imitation Learning. Frontiers in Robotics and AI.

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Xin Zhang, Tatsuya Matsushima, Yusuke Iwasawa, Yutaka Matsuo (2020). 言語指示を用いたメタ模倣学習 (Meta-Imitation Learning with Language Instruction).

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