Static versus adaptive gain control strategy for visuo-motor stabilization

Abstract

Biological principles of closed-loop motor control have gained much interest over the last years for their potential applications in robotic system. Although some progress has been made in understanding of how biological systems use sensory signals to control reflex and voluntary behaviour, experimental platforms are still missing which allow us to study sensorimotor integration under closed-loop conditions. We developed a fly-robot interface (FRI) to investigate the dynamics of a 1-DoF image stabilization task. Neural signals recorded from an identified visual interneuron were used to control a two-wheeled robot which compensated for wide-field visual image shifts caused by externally induced rotations. We compared the frequency responses of two different controllers with static and adaptive feedback gains and their performance and found that they offer competing benefits for visual stabilization. In future research will use the FRI to study how different sensor systems contribute towards robust closed-loop motor control. © 2012 Springer-Verlag.