Complex robots that interact naturally with humans require the integration, coordination and maintenance of many diverse software components and algorithms. An architecture that incorporates explicit knowledge about the relationships among these components and the overall system state can be used for introspection and consequently to reason about the best configurations of the computing environment under changing conditions; potential uses include maintaining the system's integrity, promoting its health, and providing the ability to dynamically reconfigure system components (e.g., after component failure). In this paper, we describe a rudimentary reasoning system, part of our distributed integrated affect reflection cognition (DIARC) architecture for human-robot interaction, that can autonomously perform failure detection, failure recovery, and system reconfiguration of distributed architectural components to ensure sustained operation and interactions. We demonstrate the functionality and utility of the proposed mechanisms on a robot, where architectural components are forcefully removed by hand and automatically recovered by the system while the robot is continuing its interactions with humans as part of a joint human-robot task.

Complex, autonomous robots integrate a large set of sometimes very diverse algorithms across at least three levels of system organization: the agent architecture, the implementation environment, and the hardware devices. Insofar as a distinction is maintained between them, the levels serve different purposes and thus exhibit different characteristic strengths and weaknesses. Exchanging information among organizational levels can be used to mitigate the shortcomings of one level by making use of the strengths of another. In this paper, we highlight the roles, characteristics, and relations between the infrastructure and the architecture of complex robots, describing a novel form of integration that results from enabling the exchange of information between these two levels, which otherwise is maintained internally. The information from the infrastructure is especially amenable for use by the architecture to achieve a higher level of robustness and system awareness. We demonstrate the functionality and utility of the proposed mechanisms in a set of experiments in which failures of architectural components are induced on an actual robot engaged in a joint human-robot team task.

Recognizing and responding to human affect is important in collaborative tasks in joint human-robot teams. In this paper we present an integrated affect and cognition architecture for HRI and report results from an experiment with this architecture that shows that expressing affect and responding to human affect with affect expressions can significantly improve team performance in a joint human-robot task.

Robots that can interact naturally with humans require the integration and coordination of many different components with heavy computational demands. We argue that an architecture framework with facilities for dynamic, reliable, fault-recovering, remotely accessible, distributed computing is needed for the development and operation of applications that support and enhance human activities and capabilities. We describe a robotic architecture development system, called ADE, that is built on top of a multi-agent system in order to provide all of the above features. Specifically, we discuss support for autonomic computing in ADE, briefly comparing it to related features of other commonly used robotic systems. We also report our experiences with ADE in the development of an architecture for an intelligent robot assistant and provide experimental results demonstrating the system's utility