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

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.

Investigation of the Implementation of Leader-Follower Formation Control to Bipedal Locomotion

“…The proposed controller is proven to allow the model to dynamically adapt in walking on roads with different slopes. Similarly, Auddy et al in [3] also use CPG-based model to build their biped locomotion control. They introduce a neural network-based high-level CPGbased model controller to produce a stable walking humanoid robot controller with less errors.…”

Humanoid adaptive locomotion control through a bioinspired CPG-based controller

“…Wang et al [33] proposed a control strategy to simulate hierarchical biological CPG walking control and realized quadruped robot motion control. Auddy et al [34] used an optimized CPG network for joint control and a neural network as the high-level controller to achieve bipedal locomotion in simulation. Endo et al [35] adjusted the CPG control system parameters via a policy gradient-enhanced learning control method and achieved steady walking for a biped robot.…”

Achieving natural behavior in a robot using neurally inspired hierarchical control

“…Unlike other robot control architectures that perform model-based control and planning (11, 12, 13, 14, 22, 23, 25, 33, 45, 50), our control architecture generates robust and adaptive goal-directed behavior through a simple feedback process requiring no model of the environment, prediction of future states, or learning. Unlike architectures in which behavior is generated by environmental stimuli or internal system dynamics (1, 2, 3, 4, 18, 24, 28, 29, 32, 36, 35, 37, 43, 46), our architecture generates adaptive behavior by automatically achieving continuously changing internal goals in the control hierarchy. The only computations required are operations like subtraction and integration, which can be successfully implemented by analog computing devices.…”