Evaluating Methods for End-User Creation of Robot Task Plans
This addresses the problem of making robot programming accessible to domain experts, though it is incremental as it builds on existing Behavior Tree-based systems.
The study evaluated strategies for enabling users to create perception-driven task plans for collaborative robots, finding that the SmartMove method in the CoSTAR system improved usability with an average System Usability Scale score of 73.4 and led to faster task completion and higher success rates compared to baseline versions.
How can we enable users to create effective, perception-driven task plans for collaborative robots? We conducted a 35-person user study with the Behavior Tree-based CoSTAR system to determine which strategies for end user creation of generalizable robot task plans are most usable and effective. CoSTAR allows domain experts to author complex, perceptually grounded task plans for collaborative robots. As a part of CoSTAR's wide range of capabilities, it allows users to specify SmartMoves: abstract goals such as "pick up component A from the right side of the table." Users were asked to perform pick-and-place assembly tasks with either SmartMoves or one of three simpler baseline versions of CoSTAR. Overall, participants found CoSTAR to be highly usable, with an average System Usability Scale score of 73.4 out of 100. SmartMove also helped users perform tasks faster and more effectively; all SmartMove users completed the first two tasks, while not all users completed the tasks using the other strategies. SmartMove users showed better performance for incorporating perception across all three tasks.