  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.
