# DiSCo: Diffusion Sequence Copilots for Shared Autonomy

arXiv: https://arxiv.org/abs/2603.22787 · HTML: https://mlsanity.com/paper/2603.22787/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2026.

## Built on by (most cited first, 3 of 3)

- [Flow Control: Steering Vision-Language-Action Models with Simple Real-Time Inputs](https://mlsanity.com/paper/2606.10180/) (2026)
- [Steering Autoregressive Vision-Language-Action Policies via Action Token Intervention](https://mlsanity.com/paper/2606.15021/) (2026)
- [Assistron: Bayesian Shared Autonomy with Off-the-shelf Vision-Language-Action Models](https://mlsanity.com/paper/2606.23147/) (2026)

## Abstract (arXiv)

Shared autonomy combines human user and AI copilot actions to control complex systems such as robotic arms. When a task is challenging, requires high dimensional control, or is subject to corruption, shared autonomy can significantly increase task performance by using a trained copilot to effectively correct user actions in a manner consistent with the user's goals. To significantly improve the performance of shared autonomy, we introduce Diffusion Sequence Copilots (DiSCo): a method of shared autonomy with diffusion policy that plans action sequences consistent with past user actions. DiSCo seeds and inpaints the diffusion process with user-provided actions with hyperparameters to balance conformity to expert actions, alignment with user intent, and perceived responsiveness. We demonstrate that DiSCo substantially improves task performance in simulated driving and robotic arm tasks. Project website: https://sites.google.com/view/disco-shared-autonomy/