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How can we enable users to create effective, perception-driven task plans forcollaborative robots? We conducted a 35-person user study with the BehaviorTree-based CoSTAR system to determine which strategies for end user creation ofgeneralizable robot task plans are most usable and effective. CoSTAR allowsdomain experts to author complex, perceptually grounded task plans forcollaborative robots. As a part of CoSTAR's wide range of capabilities, itallows users to specify SmartMoves: abstract goals such as "pick up component Afrom the right side of the table." Users were asked to perform pick-and-placeassembly tasks with either SmartMoves or one of three simpler baseline versionsof CoSTAR. Overall, participants found CoSTAR to be highly usable, with anaverage System Usability Scale score of 73.4 out of 100. SmartMove also helpedusers perform tasks faster and more effectively; all SmartMove users completedthe first two tasks, while not all users completed the tasks using the otherstrategies. SmartMove users showed better performance for incorporatingperception across all three tasks.

Evaluating Methods for End-User Creation of Robot Task Plans