Integration of Action, Joint Action and Learning in Robot Cognitive Architectures

In contrast with research work in Artificial Intelligence, which can test designed reasoning algorithms on specific problems in isolation during perfectly controlled discrete simulations, Robotics always faced the requirement of integrating perception, decision and action execution to work on embodied platforms interacting in and with the real world. This forced roboticists to address in various ways the question of how to efficiently coordinate different memory systems, different levels of decision-making and different learning processes within proposed artificial cognitive architectures. Such robotic proposals have often entertained strong interactions with similar questions as they are also raised in Philosophy, Neuroscience, Psychology and Cognitive Sciences in general. Here we focally review robotic work integrating different levels of action (from goal-oriented action planning to reflexes; from action monitoring to reactive visually-guided behavior) and their associated learning mechanisms. We show that such integration is necessary and sufficient to have an artificial agent fulfill a basic level of self-performance monitoring, and as a consequence to display higher behavioral flexibility, autonomy and generalizability between different environments. We illustrate these issues with examples of experimental work on coordination of multiple learning systems in a single robot and on joint action during human-robot cooperation, finding that partly similar mechanisms for the integration of different action levels can be at work for both individual action and joint action. We highlight some of the successes and failures of these robotic approaches with the hope of feeding and contributing to similar debates on action in other fields of Cognitive Sciences.