  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
