著者

室岡 貴之 濱屋 政志 フェリックス フォン ドリガルスキ 田中 一敏 井尻 善久

出版者

一般社団法人 日本ロボット学会

雑誌

日本ロボット学会誌 (ISSN:02891824)

巻号頁・発行日

vol.39, no.2, pp.177-180, 2021 (Released:2021-03-24)

参考文献数

10

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The recent growth of robotic manipulation has resulted in the realization of increasingly complicated tasks, and various kinds of learning-based approaches for planning or control have been proposed. However, learning-based approaches which can be applied to multiple environments are still an active topic of research. In this study, we aim to realize tasks in a wide range of environments by extending conventional learning-based approaches with parameters which describe various dynamics explicitly and implicitly. We applied our proposed method to two state-of-the-art learning-based approaches: deep reinforcement learning and deep model predictive control, and realized two types of non-prehensile manipulation tasks: a cart pole and object pushing, the dynamics of which are difficult to model.