Toward human-centered shared autonomy AI paradigms for human-robot teaming in healthcare
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910540380504064 |
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| author | Abiri, Reza Rabiee, Ali Ghafoori, Sima Cetera, Anna |
| author_facet | Abiri, Reza Rabiee, Ali Ghafoori, Sima Cetera, Anna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_17464 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Toward human-centered shared autonomy AI paradigms for human-robot teaming in healthcare Abiri, Reza Rabiee, Ali Ghafoori, Sima Cetera, Anna Robotics Systems and Control 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. |
| title | Toward human-centered shared autonomy AI paradigms for human-robot teaming in healthcare |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2407.17464 |