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手术体现智能用于腹腔镜机器人辅助手术中的通用化任务自主性.

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这项研究介绍了外科手术机器人的开源模拟器,使体内人工智能能够实现自主任务执行. 开发的方法证明了从模拟到现实世界手术场景的零射击转移.

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 人工智能的人工智能
  • 手术技术 手术技术

背景情况:

  • 目前的手术机器人可以自动执行特定的任务,但在各种手术环境中难以实现通用化.
  • 具体智能用于通用机器人学习是有希望的,但在医学中未得到充分利用.

研究的目的:

  • 开发和验证一个开源的外科实体智能模拟器,用于强化学习.
  • 为了使模拟训练政策的零射击转移到现实世界手术机器人.
  • 推进在微创手术中的自主能力.

主要方法:

  • 推出了一个开源的外科实体智能模拟器,用于交互式强化学习.
  • 开发了一种结合数据驱动策略和经典控制器的范式,包括视觉解析与立体声深度估计和图像分割.
  • 在视觉解析中使用视觉基础模型来处理复杂的场景.

主要成果:

  • 在达芬奇研究工具包上的七个基于游戏的任务中展示了自主技能获取.
  • 通过使用 Sentire 手术系统,实现了 ex vivo 动物组织上五项外科辅助任务的自动化.
  • 在活体动物试验中验证了在动态的体内环境中对三个任务的学习策略.

结论:

  • 开源基础设施和通用学习范式促进了对自主手术机器人体内智能的研究.
  • 拟议的方法可以在各种条件下实现强大的性能,包括不同的场景,物体大小,仪器类型和照明.
  • 这项工作为更具适应性和智能的手术机器人系统铺平了道路.