# Corrective Shared Autonomy for Addressing Task Variability

arXiv: https://arxiv.org/abs/2102.07165 · HTML: https://mlsanity.com/paper/2102.07165/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Feb 2021.

## Built on by (most cited first, 5 of 5)

- [Unified Learning from Demonstrations, Corrections, and Preferences during Physical Human-Robot Interaction](https://mlsanity.com/paper/2207.03395/) (2022)
- [Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring](https://mlsanity.com/paper/2605.30834/) (2026)
- [RoboPocket: Improve Robot Policies Instantly with Your Phone](https://mlsanity.com/paper/2603.05504/) (2026)
- [Collaborative Decision Making Using Action Suggestions](https://mlsanity.com/paper/2209.13160/) (2022)
- [From Perception to Assistance: Open-Vocabulary Shared Autonomy for Robotic Manipulation](https://mlsanity.com/paper/2607.17323/) (2026)

## Abstract (arXiv)

Many tasks, particularly those involving interaction with the environment, are characterized by high variability, making robotic autonomy difficult. One flexible solution is to introduce the input of a human with superior experience and cognitive abilities as part of a shared autonomy policy. However, current methods for shared autonomy are not designed to address the wide range of necessary corrections (e.g., positions, forces, execution rate, etc.) that the user may need to provide to address task variability. In this paper, we present corrective shared autonomy, where users provide corrections to key robot state variables on top of an otherwise autonomous task model. We provide an instantiation of this shared autonomy paradigm and demonstrate its viability and benefits such as low user effort and physical demand via a system-level user study on three tasks involving variability situated in aircraft manufacturing.