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

TL;DR

This paper introduces a hierarchical control system combining optimized central pattern generators and neural networks for bipedal robot locomotion, enabling high-level goal achievement without detailed robot models.

Contribution

It presents a novel hierarchical control architecture that separates motion generation from modulation, improving adaptability and high-level control in bipedal locomotion.

Findings

Effective in simulation with NICO robot

Operates without exact robot model

Achieves synchronized joint movements and high-level goals

Abstract

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.