Context-Driven Method in Realization of Optimized Human-Robot Interaction

Perceptual uncertainty and environmental volatility are among the most enduring challenges in robotic research today. Contemporary robotic systems are usually designed to work in specific and controlled domains where a total number of variables is defined. Traditional solutions therefore often result in over-constrained interaction spaces or rigid system architectures where any unexpected change can result in system failure. The focus of this work is set on achieving a constant adaptation of the system to changes through interaction. A computational mechanism based on the entropy reduction method is integrated along with the three-component control model. This model is seen as a context-to-data interpreter used to provide context-aware reasoning to the technical system. The mechanism is using a decrease in interaction uncertainties when proofs are provided to the system. In this way, the robot can choose the right interaction strategy that resolves reasoning ambiguities most efficiently