Simultaneous recognition and reproduction of demonstrated behavior

Predictions of sensory-motor interactions with the world is often referred to as a key component in cognition. We here demonstrate that prediction of sensory-motor events, i.e., relationships between percepts and actions, is sufficient to learn navigation skills for a robot navigating in an apartment environment. In the evaluated application, the simulated Robosoft Kompai robot learns from human demonstrations. The system builds fuzzy rules describing temporal relations between sensory-motor events recorded while a human operator is tele-operating the robot. With this architecture, referred to as Predictive Sequence Learning (PSL), learned associations can be used to control the robot and to predict expected sensor events in response to executed actions. The predictive component of PSL is used in two ways: (1) to identify which behavior that best matches current context and (2) to decide when to learn, i.e., update the confidence of different sensory-motor associations. Using this approach, knowledge interference due to over-fitting of an increasingly complex world model can be avoided. The system can also automatically estimate the confidence in the currently executed behavior and decide when to switch to an alternate behavior. The performance of PSL as a method for learning from demonstration is evaluated with, and without, contextual information. The results indicate that PSL without contextual information can learn and reproduce simple behaviors, but fails when the behavioral repertoire becomes more diverse. When a contextual layer is added, PSL successfully identifies the most suitable behavior in almost all test cases. The robot’s ability to reproduce more complex behaviors, with partly overlapping and conflicting information, significantly increases with the use of contextual information. The results support a further development of PSL as a component of a dynamic hierarchical system performing control and predictions on several levels of abstraction.