The grand challenges of learning medical robot autonomy

IF 27.5 1区计算机科学 Q1 ROBOTICS

Science Robotics Pub Date : 2025-07-30 DOI:10.1126/scirobotics.adz8279

Pierre E. Dupont, Alperen Degirmenci

引用次数: 0

Abstract

Most medical robots depend on human operators for sensing, decision-making, and action during procedures. Future progress depends on enabling robots to take on these capabilities. Although learning-based approaches provide remarkable promise toward achieving this goal, notable challenges must be addressed to unlock these robots’ full potential in clinical settings.

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学习医疗机器人自主性的巨大挑战

大多数医疗机器人在操作过程中依赖人类操作员进行感知、决策和行动。未来的进步取决于让机器人具备这些能力。尽管基于学习的方法为实现这一目标提供了显著的希望，但要在临床环境中释放这些机器人的全部潜力，必须解决显著的挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Science Robotics Mathematics-Control and Optimization

CiteScore

30.60

自引率

2.80%

发文量

期刊介绍： Science Robotics publishes original, peer-reviewed, science- or engineering-based research articles that advance the field of robotics. The journal also features editor-commissioned Reviews. An international team of academic editors holds Science Robotics articles to the same high-quality standard that is the hallmark of the Science family of journals. Sub-topics include: actuators, advanced materials, artificial Intelligence, autonomous vehicles, bio-inspired design, exoskeletons, fabrication, field robotics, human-robot interaction, humanoids, industrial robotics, kinematics, machine learning, material science, medical technology, motion planning and control, micro- and nano-robotics, multi-robot control, sensors, service robotics, social and ethical issues, soft robotics, and space, planetary and undersea exploration.