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      Two-legged robot motion control with recurrent neural networks

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      Author(s)
      Çatalbaş, Bahadır
      Morgül, Ömer
      Date
      2022-04
      Source Title
      Journal of Intelligent and Robotic Systems: Theory and Applications
      Print ISSN
      0921-0296
      Publisher
      Springer
      Volume
      104
      Issue
      4
      Pages
      59-1 - 59-30
      Language
      English
      Type
      Article
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      Abstract
      Legged locomotion is a desirable ability for robotic systems thanks to its agile mobility and wide range of motions that it provides. In this paper, the use of neural network-based nonlinear controller structures which consist of recurrent and feedforward layers have been examined in the dynamically stable walking problem of two-legged robots. In detail, hybrid neural controllers, in which long short-term memory type of neuron models employed at recurrent layers, are utilized in the feedback and feedforward paths. To train these neural networks, supervised learning data sets are created by using a biped robot platform which is controlled by a central pattern generator. Then, the ability of the neural networks to perform stable gait by controlling the robot platform is examined under various ground conditions in the simulation environment. After that, the stable walking generation capacity of the neural networks and the central pattern generators are compared with each other. It is shown that the inclusion of recurrent layer provides smooth transition and control between stance and flight motion phases and L2 regularization is beneficial for walking performance. Finally, the proposed hybrid neural network models are found to be more successful gait controllers than the central pattern generator, which is employed to generate data sets used in training. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.
      Keywords
      Robot locomotion control
      Biped robot
      Recurrent neural networks
      Long short-term memory
      Central pattern generator
      Permalink
      http://hdl.handle.net/11693/111575
      Published Version (Please cite this version)
      https://doi.org/10.1007/s10846-021-01553-5
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      • Department of Electrical and Electronics Engineering 4011
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