Toward an Interactive Reinforcement Based Learning Framework for Human Robot Collaborative Assembly Processes

As manufacturing demographics change from mass production to mass customization, advances in human-robot interaction in industries have taken many forms. However, the topic of reducing the programming effort required by an expert using natural modes of communication is still open. To answer this challenge, we propose an approach based on Interactive Reinforcement Learning that learns a complete collaborative assembly process. The learning approach is done in two steps. First step consists of modeling simple tasks that compose the assembly process, using task based formalism. The robotic system then uses these modeled simple tasks and proposes to the user a set of possible actions at each step of the assembly process via a GUI. The user then “interacts” with the robotic system by selecting an option from the given choice. The robot records the action chosen and performs it, progressing the assembly process. Thereby, the user teaches the system which task to perform when. In order to reduce the number of actions proposed, the system considers additional information such as user and robot capabilities and object affordances. These set of action proposals are further reduced by modeling the proposed actions into a goal based hierarchy and by including action prerequisites. The learning framework highlights its ability to learn a complicated human robot collaborative assembly process in a user intuitive fashion. The framework also allows different users to teach different assembly processes to the robot.