Flexible attention-based cognitive architecture for robots

Abstract

Robots have been working in factories to achieve tasks autonomously with little human intervention for some time. Even though robots are commonly found as vacuum cleaners in homes and assistants in hospitals, by comparison with factory robots, service robots have not been widely deployed in society because there remains several challenges deploying robots to achieve complex tasks in open, unstructured, uncontrolled and complex environments. Critical research gaps arise from the lack of cognitive architectures that support robots to undertake tasks in open and complex environments. Throughout the history of AI, researchers have developed various algorithms, representations and mechanisms, to solve specific tasks. However, each of these techniques has different strengths and weaknesses when applied to particular problems. A cognitive architecture provides a unifying infrastructure that can integrate various techniques to solve open and complex tasks. However, four important issues become apparent when current cognitive architectures are applied to service robotic tasks. First, they are not capable of managing robot resources and as a result robotic developers must take responsibility for managing the resources manually. Second, they are not capable of integrating independently developed techniques, which are often needed to solve problems. Third, they are inflexible, unable to adapt to design changes and require considerable time and effort to modify. Fourth, they are inadequate for supporting the necessary capabilities required by robots such as multiple goals, reliability and maintainability. These issues are confirmed when cognitive architectures are applied to a standard benchmark problem in AI: the autonomous robot soccer problem. The purpose of this dissertation is to address these significant gaps so as to accelerate the development, deployment and adoption of service robots undertaking tasks in open and complex environments. This dissertation develops a novel bio-inspired cognitive architecture (called ASMO) that has been designed and developed to address all four identified shortcomings of current cognitive architectures. In ASMO, intelligent behaviours to solve open and complex tasks is a result of the emergence of constituent processes, rather than from careful top-down control engineering. Minsky has argued in his Society of Mind that intelligent behaviours can emerge from the interaction of many simple processes, even though each process may lack `intelligence' in isolation. In addition, Anderson argued that an emergent system produces more complex behaviours and properties that cannot be reduced to the sum of its components. ASMO has attention, emotion and learning mechanisms that are inspired by human intelligence. It treats each action as a concurrent, independent and self-governed black box process that competes for the robot's attention to perform actions. The attention mechanism is used to mediate the competition among processes, which correspond to the set of potential actions. The emotion…