An important challenge in human–robot interaction (HRI) is enabling non-expert users to specify complex tasks for autonomous robots. Recently, active preference learning has been applied in HRI to interactively shape a robot’s behavior. We study a framework where users specify constraints on allowable robot movements on a graphical interface, yielding a robot task specification. However, users may not be able to accurately assess the impact of such constraints on the performance of a robot. Thus, we revise the specification by iteratively presenting users with alternative solutions where some constraints might be violated, and learn about the importance of the constraints from the users’ choices between these alternatives. We demonstrate our framework in a user study with a material transport task in an industrial facility. We show that nearly all users accept alternative solutions and thus obtain a revised specification through the learning process, and that the revision leads to a substantial improvement in robot performance. Further, the learning process reduces the variances between the specifications from different users and, thus, makes the specifications more similar. As a result, the users whose initial specifications had the largest impact on performance benefit the most from the interactive learning.

中文翻译：

---

通过主动偏好学习改进机器人行为的用户规范：框架和评估

人机交互 (HRI) 的一个重要挑战是使非专家用户能够为自主机器人指定复杂的任务。最近，主动偏好学习已应用于 HRI 以交互地塑造机器人的行为。我们研究了一个框架，其中用户在图形界面上指定对允许的机器人运动的约束，从而产生机器人任务规范。但是，用户可能无法准确评估此类约束对机器人性能的影响。因此，我们通过迭代地向用户展示可能违反某些约束的替代解决方案来修改规范，并从用户在这些替代方案之间的选择中了解约束的重要性。我们在用户研究中展示了我们的框架，其中包含工业设施中的材料运输任务。我们表明几乎所有用户都接受替代解决方案，从而通过学习过程获得修订后的规范，并且修订导致机器人性能的显着提高。此外，学习过程减少了来自不同用户的规格之间的差异，从而使规格更加相似。因此，初始规格对性能影响最大的用户从交互式学习中受益最大。