The novel approach to use meta-psychology-the theoretic foundation of psychoanalysis-as archetype for a decision making framework for autonomous agents was realized in simulations recently. In addition, multiple studies showed the capability of a robot to sense and interact in its environment. This work fills the gap between sensing, environmental interaction and decision making by grounding these topics with an agents internal needs using the concepts of meta-psychology. The bodies of typical agents are equipped with internal systems which can generate bodily needs-for example the urgent need for food. As proof-of-concept we implemented this concept on a simulated agent as well as on a physical real humanoid biped robot to additionally proof the concept within a fully controlled simulated environment. The use of the common humanoid robot platform NAO, which has 25 degrees of freedom and biped locomotion, enforced us to deal with complex situations and disturbed sensor readings. NAO provides various internal sensors like engine temperature or battery level as well as external sensors like sonar or cameras. An implemented visual marker detecting system allowed us to detect objects in the surrounding environmental, representing food or energy sources. We show, how it is possible to use the psychoanalytically inspired framework ARS to control a real world application, the robot NAO.

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To manage the increasing volume of data per time unit, achievements in information processing and artificial intelligence were made. But still the complex processes of human perception and scenario recognition are not fully understood and still far from implementation in technical applications. The contribution of this article to the field of cognitive automation is the concept of prediction for perceptual-and scenario-recognition frameworks. It is a model where prediction originates from neuro-psychoanalytical theories. Inspired by experience-based planning, which is used by the psychoanalytical decision unit, the prediction of possible outcomes from scenarios can be used for proactive acting. It results in a higher detection rate and a faster performance for recognition-units. This first implementation shows the possibilities of the concept and gives an outlook of the performance as soon as the system is fully integrated in the decision-unit.