# Assistive Teaching of Motor Control Tasks to Humans

arXiv: https://arxiv.org/abs/2211.14003 · HTML: https://mlsanity.com/paper/2211.14003/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 55 references that are in the map.
- First posted on arXiv 25 Nov 2022.

## Builds on (most cited first, 18 of 18)

- [Diversity is All You Need: Learning Skills without a Reward Function](https://mlsanity.com/paper/1802.06070/) (2018)
- [The Option-Critic Architecture](https://mlsanity.com/paper/1609.05140/) (2016)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Dynamics-Aware Unsupervised Discovery of Skills](https://mlsanity.com/paper/1907.01657/) (2019)
- [Variational Intrinsic Control](https://mlsanity.com/paper/1611.07507/) (2016)
- [Stochastic Neural Networks for Hierarchical Reinforcement Learning](https://mlsanity.com/paper/1704.03012/) (2017)
- [Variational Option Discovery Algorithms](https://mlsanity.com/paper/1807.10299/) (2018)
- [Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills](https://mlsanity.com/paper/2002.03647/) (2020)
- [Meta Learning Shared Hierarchies](https://mlsanity.com/paper/1710.09767/) (2017)
- [Shared Autonomy via Deep Reinforcement Learning](https://mlsanity.com/paper/1802.01744/) (2018)
- [Shared Autonomy via Hindsight Optimization for Teleoperation and Teaming](https://mlsanity.com/paper/1706.00155/) (2017)
- [Shared Autonomy with Learned Latent Actions](https://mlsanity.com/paper/2005.03210/) (2020)
- [Teaching Categories to Human Learners with Visual Explanations](https://mlsanity.com/paper/1802.06924/) (2018)
- [LILA: Language-Informed Latent Actions](https://mlsanity.com/paper/2111.03205/) (2021)
- [Question Generation for Adaptive Education](https://mlsanity.com/paper/2106.04262/) (2021)
- [Contrastive Reinforcement Learning of Symbolic Reasoning Domains](https://mlsanity.com/paper/2106.09146/) (2021)
- [Learning Latent Actions to Control Assistive Robots](https://mlsanity.com/paper/2107.02907/) (2021)
- [Assisted Perception: Optimizing Observations to Communicate State](https://mlsanity.com/paper/2008.02840/) (2020)

## Built on by (most cited first, 8 of 8)

- [Towards Modeling and Influencing the Dynamics of Human Learning](https://mlsanity.com/paper/2301.00901/) (2023)
- [Computational Teaching for Driving via Multi-Task Imitation Learning](https://mlsanity.com/paper/2410.01608/) (2024)
- [Generating Language Corrections for Teaching Physical Control Tasks](https://mlsanity.com/paper/2306.07012/) (2023)
- [Shared Autonomy for Proximal Teaching](https://mlsanity.com/paper/2502.19899/) (2025)
- [CANINE: Coaching Visually Impaired Users for Interactive Navigation with a Robot Guide Dog](https://mlsanity.com/paper/2605.19501/) (2026)
- [Proximal State Nudging: Reducing Skill Atrophy from AI Assistance](https://mlsanity.com/paper/2605.20355/) (2026)
- [Fix the Mind, Not the Move: Interpretable AI Assistance via Knowledge-Gap Localization](https://mlsanity.com/paper/2606.05602/) (2026)
- [Spatio-Temporal Retrieval-based Priors for Adaptive Computational Teaching in Driving](https://mlsanity.com/paper/2606.25224/) (2026)

## Abstract (arXiv)

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