# Evaluating Methods for End-User Creation of Robot Task Plans

arXiv: https://arxiv.org/abs/1811.02690 · HTML: https://mlsanity.com/paper/1811.02690/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2018.

## Built on by (most cited first, 4 of 4)

- [Cocobo: Exploring Large Language Models as the Engine for End-User Robot Programming](https://mlsanity.com/paper/2407.20712/) (2024)
- [Towards Equitable Agile Research and Development of AI and Robotics](https://mlsanity.com/paper/2402.08242/) (2024)
- [A Unified Architecture for Dynamic Role Allocation and Collaborative Task Planning in Mixed Human-Robot Teams](https://mlsanity.com/paper/2301.08038/) (2023)
- [Address Behaviour Vulnerabilities in the Next Generation of Autonomous Robots](https://mlsanity.com/paper/2103.13268/) (2021)

## Abstract (arXiv)

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.