Evaluating Methods for End-User Creation of Robot Task Plans
Evaluating Methods for End-User Creation of Robot Task Plans
复制标题
DOI：
10.1109/iros.2018.8594127
复制
发表时间：
2018-10
期刊：
影响因子：
--
通讯作者：
Chris Paxton;Felix Jonathan;Andrew Hundt;Bilge Mutlu;Gregory Hager
中科院分区：
文献类型：
--
作者：
Chris Paxton;Felix Jonathan;Andrew Hundt;Bilge Mutlu;Gregory Hager
我们如何让用户为协作机器人创建有效的、感知驱动的任务计划？我们进行了一个35人的用户研究与行为树为基础的CoSTAR系统，以确定最终用户创建的概括性机器人任务计划的策略是最有用的和effective。CoSTAR允许领域专家为协作机器人编写复杂的、基于感知的任务计划。作为CoSTAR广泛功能的一部分，它允许用户指定SmartMoves：抽象目标，如“从桌子右侧拾取组件A”。用户被要求使用SmartMoves或CoSTAR的三个简单基线版本之一来执行拾取和放置装配任务。总体而言，参与者发现CoSTAR的可用性很高，平均系统可用性量表得分为73.4分（满分100分）。SmartMove还帮助用户更快、更有效地执行任务;所有SmartMove用户都完成了前两项任务，而并非所有用户都使用其他策略完成了任务。SmartMove用户在所有三项任务中表现出更好的感知性能。
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 effctive. 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.