Hierarchical Control for Bipedal Locomotion using Central Pattern Generators and Neural Networks

2019 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), pages 13--18, doi: 10.1109/DEVLRN.2019.8850683 - Aug 2019
Associated documents :  
The complexity of bipedal locomotion may be attributed to the difficulty in synchronizing joint movements while at the same time achieving high-level objectives such as walking in a particular direction. Artificial central pattern generators (CPGs) can produce synchronized joint movements and have been used in the past for bipedal locomotion. However, most existing CPG-based approaches do not address the problem of high-level control explicitly. We propose a novel hierarchical control mechanism for bipedal locomotion where an optimized CPG network is used for joint control and a neural network acts as a high-level controller for modulating the CPG network. By separating motion generation from motion modulation, the high-level controller does not need to control individual joints directly but instead can develop to achieve a higher goal using a low-dimensional control signal. The feasibility of the hierarchical controller is demonstrated through simulation experiments using the Neuro-Inspired Companion (NICO) robot. Experimental results demonstrate the controller’s ability to function even without the availability of an exact robot model.

 

@InProceedings{AMW19, 
 	 author =  {Auddy, Sayantan and Magg, Sven and Wermter, Stefan},  
 	 title = {Hierarchical Control for Bipedal Locomotion using Central Pattern Generators and Neural Networks}, 
 	 booktitle = {2019 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob)},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {13--18},
 	 year = {2019},
 	 month = {Aug},
 	 publisher = {IEEE},
 	 doi = {10.1109/DEVLRN.2019.8850683}, 
 }