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Generalized Task Structure Learning for Collaborative Multi-Robot/Human-Robot Task Allocation
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The basis of this work is a control architecture for collaborative multi-robot systems focusing on the problem of task allocation under hierarchical constraints imposed upon a joint task. For these types of tasks, multiple robots need to dynamically coordinate their execution during the task execution. However, due to the different abilities between different robots, a single task definition is not sufficient to ensure the task can be completely without faults. In order to alleviate these concerns, we have developed a generalized task structure which is able to transfer skills of a learned task to teams of heterogeneous robots. This system uses a small number of human demonstrations to learn a hierarchical task structure on a single robot. This structure acts as a skeleton for the task which has been adapted to work on teams of heterogeneous robots through the development of a continuous-valued metric which is able to account for the robots' variable skills during the task execution. Additionally, to further emphasize the collaboration of the multi-robot team, our previous architecture is extended to include an interdependence constraint which requires explicit cooperation between agents to complete the task.Furthermore, to allow the task execution as well as the learning of the task structure to be as generalizable as possible, several efforts were made to extend the previous architecture to work with human-robot teams. First, a system was created to learn the hierarchical tasks through verbal instruction. Second, a dialogue-based fault recovery system was developed to allow for a more robust task execution. Lastly, an intent recognition system was incorporated into the architecture to allow for human-robot teams to work collaboratively on a task.Each of these extensions were validated separately through several experiments utilizing either multi-robot or human-robot teams for pick and place tasks with hierarchical constraints. The experiments included different environmental conditions in order to show the robustness of the proposed extensions to the control architecture. Combining each of these extensions together results in a generalized task structure which enables collaborative task allocation for complex, hierarchical tasks for both multi-robot and human-robot teams.