Proactive premature intention estimation for intuitive human-robot collaboration

For effective collaboration between two humans, they are required to adapt to each other according to their apparent behavior in time. Similarly for intuitive human-robot collaboration the robot is required to adapt to the human intention apparent from his behavior. In this paper, we introduce a probability-based approach that helps the robot to adapt to the human behavior and to act proactively in the ambiguous human intentions scenarios. The robot can either wait for disambiguation of the intention, requiring extra human actions or it can proactively act depending on his previous knowledge about the human behavior. The adaptive proactivity is achieved by the help of transition weights for transitions between the neighbouring states in a finite state machine. A finite state machine models the human intention. We also introduce the online update of the intention triggers for the intentions. The online intention trigger update corresponds to the online selection of the specific state of a finite state machine as the end state. The online update of the intention triggers makes the robot more proactive in the direction of premature intention estimation. Thus the transition weights and premature intention estimation makes the human-robot collaboration more intuitive.