Object Recognition, Dynamic Contact Simulation, Detection, and Control of the Flexible Musculoskeletal Hand Using a Recurrent Neural Network With Parametric Bias
| Autor: | Yuki Asano, Koji Kawasaki, Kei Tsuzuki, Moritaka Onitsuka, Kento Kawaharazuka, Kei Okada, Masayuki Inaba |
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| Rok vydání: | 2020 |
| Předmět: |
Control and Optimization
Artificial neural network Computer science business.industry Mechanical Engineering Biomedical Engineering Cognitive neuroscience of visual object recognition Initialization Object (computer science) Computer Science Applications Task (project management) Human-Computer Interaction Recurrent neural network Artificial Intelligence Control and Systems Engineering Computer vision Computer Vision and Pattern Recognition Artificial intelligence Control (linguistics) business Parametric statistics |
| Zdroj: | IEEE Robotics and Automation Letters. 5:4580-4587 |
| ISSN: | 2377-3774 |
| DOI: | 10.1109/lra.2020.3002199 |
| Popis: | The flexible musculoskeletal hand is difficult to modelize, and its model can change constantly due to deterioration over time, irreproducibility of initialization, etc. Also, for object recognition, contact detection, and contact control using the hand, it is desirable not to use a neural network trained for each task, but to use only one integrated network. Therefore, we develop a method to acquire a sensor state equation of the musculoskeletal hand using a recurrent neural network with parametric bias. By using this network, the hand can realize recognition of the grasped object, contact simulation, detection, and control, and can cope with deterioration over time, irreproducibility of initialization, etc. by updating parametric bias. We apply this study to the hand of the musculoskeletal humanoid Musashi and show its effectiveness. |
| Databáze: | OpenAIRE |
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