Legged robot locomotion and gymnastics

The learning and control space of real-world autonomous agents are often many-dimensional, growing, and unbounded in nature. Such agents exhibit adaptive, incremental, exploratory, and sometimes explosive learning behaviors. Learning in adaptive neurofuzzy control, however, is often referred to as global training with a large set of random examples and a very low learning rate. This type of controller is not reorganizable; it cannot explain exploratory learning behaviors as exhibited by human and animal species. A theory of coordinated computational intelligence (CCI) is proposed in this paper which leads to a reorganizable multiagent cerebellar architecture for intelligent control. The architecture is based on the hypotheses that (1) a cerebellar system consists of a school of relatively simple and cognitively identifiable semiautonomous neurofuzzy agents; (2) autonomous control is the result of cerebellar agent fine-tuning and coordination rather than complicate computation; and (3) learning is accomplished via individual cerebellar agent learning and coordinated discovery in a learning-tuning-brainstorming process. Agent oriented decomposition and coordination algorithms are introduced; necessary and sufficient conditions are established for cerebellar agent discovery and common sense cerebellar motion law discovery. Nesting, safety, layering, and autonomy-four principles are analytically formulated for the reorganization of neurofuzzy agents.