Human Robot Interaction for Multi-robot Systems

The multi-robot manipulation task, where a team of robots cooperatively lift and move objects, is exceptionally difficult due to the synchronization between the robots. Although increasing the number of robots can expand the area that the robots can cover within a bounded period of time, a poor human-robot interface will ultimately compromise the performance of the team of robots. However, introducing a human operator to the team of robots, does not automatically improve performance due to the difficulty of teleoperating mobile robots with manipulators. The human operator’s concentration is divided not only among multiple robots but also between controlling each robot’s base and arm. This complexity substantially increases the potential neglect time, since the operator’s inability to effectively attend to each robot during a critical phase of the task leads to a significant degradation in task performance. In this dissertation, I propose to develop an agent-based user interface for multi-robot manipulation interface that identifies and manages any single robot that is not currently being controlled by the human operator, as well a learning from demonstration technique with the robots learning from the pre recorded macro representation of the specified task. Propagating the user’s macro commands from the actively-controlled robot to the neglected robot allows the neglected robot to leverage this control information and position itself effectively without direct human supervision. The objective of robot learning from demonstration is to have a robot learn from watching a demonstration of the task to be performed. A policy is calculated based on the task learned from demonstrations in which the user verifies that the robot has executed the macro correctly in a new situation.