Recent works on shared autonomy and assistive-AI technologies, such as assistive robot teleoperation, seek to model and help human users with limited ability in a fixed task. However, these approaches often fail to account for humans' ability to adapt and eventually learn how to execute a control task themselves. Furthermore, in applications where it may be desirable for a human to intervene, these methods may inhibit their ability to learn how to succeed with full self-control. In this paper, we focus on the problem of assistive teaching of motor control tasks such as parking a car or landing an aircraft. Despite their ubiquitous role in humans' daily activities and occupations, motor tasks are rarely taught in a uniform way due to their high complexity and variance. We propose an AI-assisted teaching algorithm that leverages skill discovery methods from reinforcement learning (RL) to (i) break down any motor control task into teachable skills, (ii) construct novel drill sequences, and (iii) individualize curricula to students with different capabilities. Through an extensive mix of synthetic and user studies on two motor control tasks -- parking a car with a joystick and writing characters from the Balinese alphabet -- we show that assisted teaching with skills improves student performance by around 40% compared to practicing full trajectories without skills, and practicing with individualized drills can result in up to 25% further improvement. Our source code is available at https://github.com/Stanford-ILIAD/teaching
翻译:最近关于共享自主性和辅助性AI技术(如辅助机器人远程操作)的著作,涉及共享自主性和辅助性AI技术,例如辅助性机器人远程操作,寻求在固定任务中模拟和帮助能力有限的人类用户,然而,这些方法往往不能说明人类适应并最终学会如何自行执行控制任务的能力。此外,在人们宜于干预的应用程序中,这些方法可能妨碍他们学习如何以完全自我控制的方式取得成功的能力。在本文件中,我们侧重于协助教授机动控制任务(如停车或着陆飞机)的问题。尽管机动任务在人类日常活动和职业中作用普遍,但是由于高度复杂和差异,很少以统一的方式教授机动任务。我们提议由AI辅助式教学算法来利用技能发现方法,从强化学习(RL)到(i)到(i)将任何运动控制任务分解成可传授的技能,(ii)建立新的运动钻机序列,以及(iii)将课程分解给能力不同的学生。通过对两种机动控制任务进行广泛的合成和用户研究组合 -- 泊车和旋转杆和写字符,从Balines/Slane redustrables)到完全的学习技能,我们通过不完善的钻练来显示个人的钻练,我们可以以学习技能,我们学习的钻练到完全的钻练。