Generalization of movements in quadruped robot locomotion by learning specialized motion data
| Autor: | Hiroki Yamamoto, Sungi Kim, Yuichiro Ishii, Yusuke Ikemoto |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Quadruped robot
Gait pattern Movement decomposition Machine learning Autoencoder Technology Mechanical engineering and machinery TJ1-1570 Control engineering systems. Automatic machinery (General) TJ212-225 Machine design and drawing TJ227-240 Technology (General) T1-995 Industrial engineering. Management engineering T55.4-60.8 Automation T59.5 Information technology T58.5-58.64 |
| Zdroj: | ROBOMECH Journal, Vol 7, Iss 1, Pp 1-14 (2020) |
| Druh dokumentu: | article |
| ISSN: | 2197-4225 |
| DOI: | 10.1186/s40648-020-00174-1 |
| Popis: | Abstract Machines that are sensitive to environmental fluctuations, such as autonomous and pet robots, are currently in demand, rendering the ability to control huge and complex systems crucial. However, controlling such a system in its entirety using only one control device is difficult; for this purpose, a system must be both diverse and flexible. Herein, we derive and analyze the feature values of robot sensor and actuator data, thereby investigating the role that each feature value plays in robot locomotion. We conduct experiments using a developed quadruped robot from which we acquire multi-point motion information as the movement data; we extract the features of these movement data using an autoencoder. Next, we decompose the movement data into three features and extract various gait patterns. Despite learning only the “walking” movement, the movement patterns of trotting and bounding are also extracted herein, which suggests that movement data obtained via hardware contain various gait patterns. Although the present robot cannot locomote with these movements, this research suggests the possibility of generating unlearned movements. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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