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Implementation of Imitation Learning using Natural Learner Central Pattern Generator Neural Networks.

Hamed Shahbazi1, Reyhaneh Parandeh2, Kamal Jamshidi2

  • 1Department of Mechanical Engineering, University of Isfahan, Isfahan, Iran.

Neural Networks : the Official Journal of the International Neural Network Society
|September 7, 2016
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This study introduces Natural Learner Central Pattern Generator Neural Networks (NLCPGNN) for generating complex oscillatory patterns. The novel system effectively learns rhythmic movements and demonstrates robustness in robotic applications.

Keywords:
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Area of Science:

  • Robotics
  • Computational Neuroscience
  • Machine Learning

Background:

  • Oscillatory patterns are fundamental to biological motor control.
  • Existing neural network models often struggle with complex rhythmic motion generation and adaptive learning.
  • Central Pattern Generators (CPGs) provide a biological inspiration for generating rhythmic behaviors.

Purpose of the Study:

  • To introduce a novel neural network architecture, Natural Learner CPG Neural Networks (NLCPGNN), capable of generating complex oscillatory patterns.
  • To develop a natural policy gradient learning algorithm for simultaneously optimizing network weights and topology.
  • To demonstrate the system's ability to learn and adapt rhythmic movements in robotic systems.

Main Methods:

  • Design of O-neurons with oscillatory transfer functions as the fundamental network unit.
  • Coupling and connecting O-neurons to form a network structure.
  • Application of a natural policy gradient learning algorithm for simultaneous weight and topology optimization.
  • Utilizing a two-layer system for complex motion acquisition and rhythmic trajectory learning.

Main Results:

  • The NLCPGNN system successfully learned complex motions and rhythmic trajectories.
  • The two-layer system exhibited features of a learner model, including resistance to perturbations and modulation of amplitude/frequency.
  • Simulations in WEBOTS (linked with MATLAB) and implementation on a NAO robot confirmed high accuracy and convergence.
  • The proposed system demonstrated a high convergence rate and low test errors.

Conclusions:

  • The developed NLCPGNN with its natural policy gradient learning algorithm offers an effective approach for generating complex oscillatory patterns.
  • The system shows significant potential for robotic applications requiring adaptive and robust rhythmic motion control.
  • The findings highlight the system's capability for accurate learning and high performance in real-world robotic tasks.