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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.
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Considerations for End-User Development in the Caregiving Domain
As service robots become more capable of autonomous behaviors, it becomes increasingly important to consider how people will be able to communicate with a robot about what task it should perform and how to do the task. There has been a rise in attention to end-user development (EUD), where researchers create interfaces that enable non-roboticist end users to script tasks for autonomous robots to perform. Currently, state-of-the-art interfaces are largely constrained, often through simplified domains or restrictive end-user interaction. Motivated by our past qualitative design work exploring how to integrate a care robot in an assisted living community, we discuss challenges of EUD in this complex domain. One set of challenges stems from different user-facing representations, e.g., certain tasks may lend themselves better to a rule-based trigger-action representations, whereas other tasks may be easier to specify via a sequence of actions. The other stems from considering the needs of multiple stakeholders, e.g., caregivers and residents of the facility may all create tasks for the robot, but the robot may not be able to share information about all tasks with all residents due to privacy concerns. We present scenarios that illustrate these challenges and also discuss possible solutions.
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- Award ID(s):
- 1925043
- PAR ID:
- 10536755
- Publisher / Repository:
- Vol. 2 No. 1: Proceedings of the 2023 AAAI Fall Symposia
- Date Published:
- Journal Name:
- Proceedings of the AAAI Symposium Series
- Volume:
- 2
- Issue:
- 1
- ISSN:
- 2994-4317
- Page Range / eLocation ID:
- 532 to 536
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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 m ore effectively; all SmartMove users completed the first two tasks, while not all users completed the tasks using other strategies. SmartMove users showed better performance vs. baseline methods for incorporating perception across all three tasks.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1609/aaaiss.v2i1.27725
