# Toward human-centered shared autonomy AI paradigms for human-robot teaming in healthcare

arXiv: https://arxiv.org/abs/2407.17464 · HTML: https://mlsanity.com/paper/2407.17464/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2024.

## Built on by (most cited first, 1 of 1)

- [Learning Multimodal AI Algorithms for Amplifying Limited User Input into High-dimensional Control Space](https://mlsanity.com/paper/2505.11366/) (2025)

## Abstract (arXiv)

With recent advancements in AI and computation tools, intelligent paradigms emerged to empower different fields such as healthcare robots with new capabilities. Advanced AI robotic algorithms (e.g., reinforcement learning) can be trained and developed to autonomously make individual decisions to achieve a desired and usually fixed goal. However, such independent decisions and goal achievements might not be ideal for a healthcare robot that usually interacts with a dynamic end-user or a patient. In such a complex human-robot interaction (teaming) framework, the dynamic user continuously wants to be involved in decision-making as well as introducing new goals while interacting with their present environment in real-time. To address this challenge, an adaptive shared autonomy AI paradigm is required to be developed for the two interactive agents (Human & AI agents) with a foundation based on human-centered factors to avoid any possible ethical issues and guarantee no harm to humanity.