Managing Complexity in Large Learning Robotic Systems

Abstract

Autonomous learning systems of significant complexity often consist of several interacting modules or agents. These modules collaborate to produce a system which, when viewed as a whole, exhibit behaviour that can be interpreted in some way as learning. In designing these systems, the complexity of the interactions of large numbers of modules can become overwhelming, making debugging difficult and obscuring the workings of the system when viewed from an architectural level. A way of controlling system complexity called the Layered Learning System architecture (LLS) has been developed, which offers the advantages of incremental development and testing, easier debugging and progressive upgrading facilitation. A hexapod robot has been implemented using LLS principles, with the main learning task being that of learning to walk as fast as possible without falling over.