WEKO3
アイテム
Goal-Conditioned Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes
http://hdl.handle.net/10228/00008467
http://hdl.handle.net/10228/00008467a9b616fb-f4b1-44b6-b676-e4dcbd849e3d
| 名前 / ファイル | ライセンス | アクション |
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neuro_16.pdf (1.8 MB)
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| アイテムタイプ | 学術雑誌論文 = Journal Article(1) | |||||||||||||||||||||
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| 公開日 | 2021-09-16 | |||||||||||||||||||||
| 資源タイプ | ||||||||||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||||||||||
| 資源タイプ | journal article | |||||||||||||||||||||
| タイトル | ||||||||||||||||||||||
| タイトル | Goal-Conditioned Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes | |||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||
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| 言語 | eng | |||||||||||||||||||||
| 著者 |
長, 隆之
× 長, 隆之
WEKO
25557
× 池本, 周平
WEKO
30354
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| 内容記述タイプ | Abstract | |||||||||||||||||||||
| 内容記述 | Imitation learning is an intuitive approach for teaching motion to robotic systems. Although previous studies have proposed various methods to model demonstrated movement primitives, one of the limitations of existing methods is that the shape of the trajectories is encoded in high dimensional space. The high dimensionality of the trajectory representation can be a bottleneck in the subsequent process such as planning a sequence of primitive motions. We address this problem by learning the latent space of the robot trajectory. If the latent variable of the trajectories can be learned, it can be used to tune the trajectory in an intuitive manner even when the user is not an expert. We propose a framework for modeling demonstrated trajectories with a neural network that learns the low-dimensional latent space. Our neural network structure is built on the variational autoencoder (VAE) with discrete and continuous latent variables. We extend the structure of the existing VAE to obtain the decoder that is conditioned on the goal position of the trajectory for generalization to different goal positions. Although the inference performed by VAE is not accurate, the positioning error at the generalized goal position can be reduced to less than 1 mm by incorporating the projection onto the solution space. To cope with requirement of the massive training data, we use a trajectory augmentation technique inspired by the data augmentation commonly used in the computer vision community. In the proposed framework, the latent variables that encodes the multiple types of trajectories are learned in an unsupervised manner, although existing methods usually require label information to model diverse behaviors. The learned decoder can be used as a motion planner in which the user can specify the goal position and the trajectory types by setting the latent variables. The experimental results show that our neural network can be trained using a limited number of demonstrated trajectories and that the interpretable latent representations can be learned. | |||||||||||||||||||||
| 言語 | en | |||||||||||||||||||||
| 書誌情報 |
en : SN Computer Science 巻 1, p. 303-1-303-10, 発行日 2020-09-16 |
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| 出版社 | ||||||||||||||||||||||
| 出版者 | Springer | |||||||||||||||||||||
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| 関連タイプ | isVersionOf | |||||||||||||||||||||
| 識別子タイプ | DOI | |||||||||||||||||||||
| 関連識別子 | https://doi.org/10.1007/s42979-020-00324-7 | |||||||||||||||||||||
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| 主題Scheme | NDC | |||||||||||||||||||||
| 主題 | 548 | |||||||||||||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||||||||||||
| 収録物識別子 | 2661-8907 | |||||||||||||||||||||
| 著作権関連情報 | ||||||||||||||||||||||
| 権利情報 | Copyright (c) Springer Nature Singapore Pte Ltd 2020 | |||||||||||||||||||||
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| 主題Scheme | Other | |||||||||||||||||||||
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| 主題Scheme | Other | |||||||||||||||||||||
| 主題 | large-memory VMs | |||||||||||||||||||||
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| 主題 | partial migration | |||||||||||||||||||||
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| 主題 | remote paging | |||||||||||||||||||||
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| 出版タイプ | AM | |||||||||||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||||||||||||
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| 値 | yes | |||||||||||||||||||||
| 連携ID | ||||||||||||||||||||||
| 値 | 8973 | |||||||||||||||||||||
