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H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions


Oct 22, 2022
Kei Ota, Hsiao-Yu Tung, Kevin A. Smith, Anoop Cherian, Tim K. Marks, Alan Sullivan, Asako Kanezaki, Joshua B. Tenenbaum


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Object Memory Transformer for Object Goal Navigation


Mar 24, 2022
Rui Fukushima, Kei Ota, Asako Kanezaki, Yoko Sasaki, Yusuke Yoshiyasu

* 7 pages, 3 figures, Accepted at ICRA 2022 

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OPIRL: Sample Efficient Off-Policy Inverse Reinforcement Learning via Distribution Matching


Sep 09, 2021
Hana Hoshino, Kei Ota, Asako Kanezaki, Rio Yokota

* Under submission 

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Training Larger Networks for Deep Reinforcement Learning


Feb 16, 2021
Kei Ota, Devesh K. Jha, Asako Kanezaki

* Under submission 

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Towards Human-Level Learning of Complex Physical Puzzles


Nov 14, 2020
Kei Ota, Devesh K. Jha, Diego Romeres, Jeroen van Baar, Kevin A. Smith, Takayuki Semitsu, Tomoaki Oiki, Alan Sullivan, Daniel Nikovski, Joshua B. Tenenbaum

* 10 pages, 7 figures 

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Deep Reactive Planning in Dynamic Environments


Nov 05, 2020
Kei Ota, Devesh K. Jha, Tadashi Onishi, Asako Kanezaki, Yusuke Yoshiyasu, Yoko Sasaki, Toshisada Mariyama, Daniel Nikovski

* 15 pages, 5 figures. Accepted at CoRL 2020 

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Efficient Exploration in Constrained Environments with Goal-Oriented Reference Path


Mar 03, 2020
Kei Ota, Yoko Sasaki, Devesh K. Jha, Yusuke Yoshiyasu, Asako Kanezaki

* 8 pages, 10 figures 

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Can Increasing Input Dimensionality Improve Deep Reinforcement Learning?


Mar 03, 2020
Kei Ota, Tomoaki Oiki, Devesh K. Jha, Toshisada Mariyama, Daniel Nikovski

* 11 pages, 10 figures 

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Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning


Mar 13, 2019
Kei Ota, Devesh K. Jha, Tomoaki Oiki, Mamoru Miura, Takashi Nammoto, Daniel Nikovski, Toshisada Mariyama

* 8 pages, 6 figures 

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