Autonomous control system and method using embodied homeostatic feedback in an operating environment

A machine-learning control system comprising an operating environment and a sensorium informationally coupled the operating environment. The sensorium comprises a set of sensors and a set of motors, both informationally coupled to a homeostatic network capable of achieving ultrastability within the operating environment. The control system builds a generative model of the operating environment by extracting, through sensorimotor feedback, state information relevant to network ultrastability associated with a particular control behavior and a set of environmental parameters identified within the operating environment. A modulating sensorimotor carrier wave signal may optionally be used to increase training speed of the machine-learning control system. The control system is adaptable to a variety of engineering solutions for autonomous control systems and data processing, such as, for example, autonomous vehicles, robotics, calibration, language processing, and computer vision. A homeostatic network debugger and automatic network topology generation algorithms using node-splitting conditions and functions are also described.