Integration of high-level cognitive functions in an autonomous robot control architecture

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Over the last decade, mobile robots have evolved from being rigid, two-dimensional navigation platforms, to compliant and interactive machines that can accomplish more complex and sophisticated tasks, assuming that they can exploit their advanced capabilities intelligently. The ultimate goal is for such robot to be able to decide on its own what, when and how they can fulfill their duties in a secure, efficient and productive manner, with minimal human intervention. I have been developing a mobile platform that brings the necessary compliance, robustness and adaptiveness to handle autonomously the unconstrained and unpredictable situations of the real world. The platform is a compliant omnidirectional humanoid mobile robot capable of natural reciprocal interaction (motion, navigation, auditory, visual, language, touch, affect) with humans, and provides an excep- tional and advanced testbed to address the integration and coordination issues that a robot control architecture must face to operate in real life settings. The focus of this proposal is on the memorization and categorization of the perception and the situations experienced by the robot, 2 key high-level cognitve functions. Memory is fundamental to reasoning, learning and cognition, but it still remains a poorly understood but required mecha- nism for robots having to understand its interactions in dynamic environments. I will study and develop approa- ches that will be integrated into a hybrid behavior-based architecture, to: exploit the concepts of short-term and long-term memory to achieve real-time processing and to derive a spatio-temporal representation of the robot's experiences from internal and perceived data; memorize and categorize events through the combination of visual, audible and tactile information; regulate satisfaction of the robot's intentions using emotional signaling, which can in term be used to categorize behavioral strategies. These approaches will be evaluated in the context of making an interactive robot assistant that will have to operate in our facility for the longest time possible. Performance will be measured in terms of the added capabilities brought by the high-level control mechanisms, and from the robot's overall ability to operate autonomously in open environments.