Learning Macros for Multi-Robot Manipulation Tasks

In this paper, we present a paradigm for allowing subjects to configure a user interface for multi-robot manipulation tasks. Multi-robot manipulation tasks can be complicated, due to the need for tight temporal coupling between the robots. However, this is an ideal scenario for human-agent-robot teams, since performing all of the manipulation aspects of the task autonomously is not feasible without additional sensors. In the best case, humans perform the delicate manipulation sections of the task, robots autonomously execute the repetitive driving, and the agents supporting the coordination through shared information propagation. Though the task itself is complicated, it is imperative that the user interface not be unreasonably complex. To ameliorate this problem, we introduce a macro acquisition system for learning combined manipulation/driving tasks. Learning takes place within this social setting; the human demonstrates the task to the single robot, but the robot uses an internal teamwork model to modify the macro to account for the actions of the second robot during execution. This allows the same macro to be useful in a variety of cooperative situations. In this paper, we show that our system is highly effective at empowering human-agent-robot teams within a household multi-robot manipulation setting and is rated favorably over a non-configurable user interface by a significant portion of the users.