Multi-Modal Motion Activation for Robot Control Using Spiking Neurons

Robot control is an active field of research, in particular for humanoid robots which present challenging problems. Yet, few of the proposed methods exhibit properties of biological systems. Neurorobotics presents an interesting approach by using neural models and mechanisms of brain function to control robots. In this paper, we present a novel way to activate robot motions with different modalities, inspired by biology and implemented with spiking neurons. We focus on two specific characteristics of biological systems for motion control: the hierarchical representation, which is distributed in the body and the nervous system, and the different activation modalities. We modeled three different activation modalities: voluntary, rhythmic and reflexes. In our architecture, motions are represented with motor primitives that can be combined and parameterized. A mechanism to learn new motions based on previous knowledge is incorporated using an error function. Our approach is evaluated controlling a robotic arm in simulation. We show that each activation modality works, and we show that they can be combined in various ways in different scenarios.