Perceptual Control with Large Feature and Actuator Networks
This work addresses the challenge of neuromorphic feedback control for robotics, but the results are largely conceptual and incremental, lacking concrete performance numbers.
The paper develops a control theory for systems with large numbers of simple agents, showing that having many imprecise sensors and actuators can be advantageous for tasks like robot navigation using optical flow. It demonstrates robust steering laws using averaged noisy inputs from many sensors.
This paper discusses elements of a control theory of systems comprised of networks of simple agents that collectively achieve sensing and actuation goals despite having strictly limited capability when acting alone. The goal is to understand {\em neuromorphic} feedback control in which streams of data come from large arrays of sensors (e.g. photo-receptors in the eye) and actuation requires coordination of large numbers of actuators (e.g. motor neurons). The context for this work is set by consideration of a stylized problem of robot navigation that uses optical flow as sensed by two idealized and precise photoreceptors. A robust steering law in this setting establishes a foundation for exploiting optical flow based on averaged noisy inputs from large numbers of imprecise sensing elements. Seeking inspiration in neurobiology, the challenges of actuator and sensor intermittency are discussed as are learning actuator coordination strategies. It is shown that there are advantages to having large numbers of control inputs and outputs. The results will be shown to make contact with ideas from control communication complexity and the standard parts problem.