# Shared Autonomy for Proximal Teaching

arXiv: https://arxiv.org/abs/2502.19899 · HTML: https://mlsanity.com/paper/2502.19899/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 66 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 23 of 23)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Modular Multitask Reinforcement Learning with Policy Sketches](https://mlsanity.com/paper/1611.01796/) (2016)
- [Shared Autonomy via Deep Reinforcement Learning](https://mlsanity.com/paper/1802.01744/) (2018)
- [TACO: Learning Task Decomposition via Temporal Alignment for Control](https://mlsanity.com/paper/1803.01840/) (2018)
- ["No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy](https://mlsanity.com/paper/2301.02555/) (2023)
- [Controlling Assistive Robots with Learned Latent Actions](https://mlsanity.com/paper/1909.09674/) (2019)
- [LILA: Language-Informed Latent Actions](https://mlsanity.com/paper/2111.03205/) (2021)
- [Multi-Armed Bandits for Intelligent Tutoring Systems](https://mlsanity.com/paper/1310.3174/) (2013)
- [LESS is More: Rethinking Probabilistic Models of Human Behavior](https://mlsanity.com/paper/2001.04465/) (2020)
- [Situational Confidence Assistance for Lifelong Shared Autonomy](https://mlsanity.com/paper/2104.06556/) (2021)
- [Language-guided Skill Learning with Temporal Variational Inference](https://mlsanity.com/paper/2402.16354/) (2024)
- [Assistive Teaching of Motor Control Tasks to Humans](https://mlsanity.com/paper/2211.14003/) (2022)
- [Decision-Making in Driver-Automation Shared Control: A Review and Perspectives](https://mlsanity.com/paper/2007.12597/) (2020)
- [MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communication](https://mlsanity.com/paper/2203.02877/) (2022)
- [COACH: Cooperative Robot Teaching](https://mlsanity.com/paper/2302.06199/) (2023)
- [Proximal Curriculum with Task Correlations for Deep Reinforcement Learning](https://mlsanity.com/paper/2405.02481/) (2024)
- [Active Intent Disambiguation for Shared Control Robots](https://mlsanity.com/paper/2005.03652/) (2020)
- [ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations](https://mlsanity.com/paper/1910.12154/) (2019)
- [TeachingBot: Robot Teacher for Human Handwriting](https://mlsanity.com/paper/2309.11848/) (2023)
- [T-SKIRT: Online Estimation of Student Proficiency in an Adaptive Learning System](https://mlsanity.com/paper/1702.04282/) (2017)
- [E-Gotsky: Sequencing Content using the Zone of Proximal Development](https://mlsanity.com/paper/1904.12268/) (2019)
- [Training Reinforcement Learning Agents and Humans With Difficulty-Conditioned Generators](https://mlsanity.com/paper/2312.02309/) (2023)
- [Integrating Flow Theory and Adaptive Robot Roles: A Conceptual Model of Dynamic Robot Role Adaptation for the Enhanced Flow Experience in Long-term Multi-person Human-Robot Interactions](https://mlsanity.com/paper/2401.02833/) (2024)

## Built on by (most cited first, 4 of 4)

- [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)
- [Spatio-Temporal Retrieval-based Priors for Adaptive Computational Teaching in Driving](https://mlsanity.com/paper/2606.25224/) (2026)
- [AI Coaching for Accelerating Human Skill Development with Reinforcement Learning](https://mlsanity.com/paper/2606.25337/) (2026)

## Abstract (arXiv)

Motor skill learning often requires experienced professionals who can provide personalized instruction. Unfortunately, the availability of high-quality training can be limited for specialized tasks, such as high performance racing. Several recent works have leveraged AI-assistance to improve instruction of tasks ranging from rehabilitation to surgical robot tele-operation. However, these works often make simplifying assumptions on the student learning process, and fail to model how a teacher's assistance interacts with different individuals' abilities when determining optimal teaching strategies. Inspired by the idea of scaffolding from educational psychology, we leverage shared autonomy, a framework for combining user inputs with robot autonomy, to aid with curriculum design. Our key insight is that the way a student's behavior improves in the presence of assistance from an autonomous agent can highlight which sub-skills might be most ``learnable'' for the student, or within their Zone of Proximal Development. We use this to design Z-COACH, a method for using shared autonomy to provide personalized instruction targeting interpretable task sub-skills. In a user study (n=50), where we teach high performance racing in a simulated environment of the Thunderhill Raceway Park with the CARLA Autonomous Driving simulator, we show that Z-COACH helps identify which skills each student should first practice, leading to an overall improvement in driving time, behavior, and smoothness. Our work shows that increasingly available semi-autonomous capabilities (e.g. in vehicles, robots) can not only assist human users, but also help *teach* them.