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

TL;DR

This paper discusses developing human-centered shared autonomy AI paradigms for healthcare robots, emphasizing real-time human involvement and ethical considerations in dynamic human-robot teaming environments.

Contribution

It proposes an adaptive shared autonomy framework grounded in human-centered factors to improve decision-making and ethical safety in healthcare robot interactions.

Findings

Highlights the need for real-time human involvement in robot decision-making

Proposes a human-centered adaptive autonomy paradigm

Addresses ethical issues in human-robot teaming

Abstract

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.