Journal
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: THEORETICAL NEURAL COMPUTATION, PT I
Volume 11727, Issue -, Pages 698-710Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-30487-4_53
Keywords
Brain inspired computing; Reinforcement learning; Artificial neural networks
Funding
- Human Frontier Science Program [RGP0002/2017]
- Vidyasirimedhi Institute of Science & Technology (VISTEC)
- European Community H2020 Programme (Future and Emerging Technologies, FET) [732266]
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In this paper, we employ a central pattern generator (CPG) driven radial basis function network (RBFN) based controller to learn optimized locomotion for a complex dung beetle-like robot using reinforcement learning approach called Policy Improvement with Path Integrals (PI2). Our CPG driven RBFN controller is inspired by rhythmic dynamic movement primitives (DMPs). The controller can be also seen as an extension to a traditional CPG controller, which usually controls only the frequency of the motor patterns but not the shape. Our controller uses the CPG to control the frequency while the RBFN takes care of the shape of the motor patterns. In this paper, we only focus on the shape of the motor patterns and optimize those with respect to walking speed and energy efficiency. As a result, the robot can travel faster and consume less power than using only the CPG controller.
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