Neural dynamics of hierarchically organized sequences: A robotic implementation

Robotic researchers face fundamental challenges when designing autonomous humanoid robots, which are able to interact with real dynamic environments. In such unstructured environments, the robot has to autonomously segment objects, detect and categorize relevant situations, decide when to initiate and terminate actions. As humans are very good in these tasks, inspiration from models of human sensory-motor and cognitive processes may help design more flexible and autonomous robotic control architectures. Recently, we have extended a neurally-inspired model for sequential organization with a representation of hierarchies of behaviors. Here, we implement this model on a robotic platform and demonstrate its functionality under constraints of a real-world implementation. The architecture generates hierarchically organized behavioral sequences on the Aldebaran´s humanoid robot NAO. The key dynamic components of serial organization - such as the intention, condition of satisfaction (CoS), and interactions within the hierarchy - are coupled to robotic sensors and motors and bring about flexible and autonomous behavior. We also demonstrate how continuous in time neural-dynamic parts of the controller may be seamlessly integrated with preprogramed algorithmic behaviors, introducing flexibility, autonomy, and ability to learn, while avoiding unnecessary complexity of the architecture.