Determining the benefit of human input in human-in-the-loop robotic systems

In this work, we analyze the pick and place task for a human-in-the-loop robotic system to determine where human input can be most beneficial to a collaborative task. This is accomplished by implementing a pick and place task on a commercial robotic arm system and determining which segments of the task, when replaced by human guidance, provide the most improvement to overall task performance and require the least cognitive effort. The pick and place task can be segmented into two main areas: coarse approach towards goal object and fine pick motion. For the fine picking phase, we look at the importance of user guidance in terms of position and orientation of the end effector. Results from our experiment show that the most successful strategy for our human-in-the-loop system is the one in which the human specifies a general region for grasping, and the robotic system completes the remaining elements of the task. Our experimental setup and procedures could be generalized and used to guide similar analysis of human impact in other human-in-the-loop systems performing other tasks.