SRT-H:通过语言条件模仿学习实现自主外科手术的层次框架
Ji Woong Brian Kim1, Juo-Tung Chen1, Pascal Hansen1
1Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD 21218, USA.
Science robotics
|July 9, 2025
概括
这项研究引入了自主手术的层次框架,使机器人能够100%成功地执行胆囊切除术等复杂手术. 该系统使用基于语言的规划来改善现实世界手术任务中的错误恢复.
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:
- 目前的自主手术研究仅限于在受控环境中的简单任务.
- 现实世界中的手术需要灵巧,长期的操作,以及适应组织变化的能力,而现有的方法却很难做到这一点.
研究的目的:
- 开发一个对灵巧,长视野的自主外科手术程序的层次框架.
- 使机器人能够将人类组织的变化概括起来,并从错误中恢复.
主要方法:
- 一个分层的框架,具有高层次的任务规划政策和低层次的轨迹生成政策.
- 高级规划器使用语言空间进行任务指令和错误纠正.
- 通过ex vivo胆囊切除实验和废除研究进行验证.
主要成果:
- 在8个胆囊上实现了100%的自主ex vivo胆囊切除成功率.
- 在动态的外科环境中,从低于最佳状态的错误恢复得到了改进.
- 层次化的方法增强了对现实的外科应用的政策稳定性.
结论:
- 拟议的框架允许在外科手术过程中逐步实现自主.
- 这项研究标志着自主手术系统在临床部署方面取得了重大进展.
- 语言引导的层次方法对于复杂的,长时间的外科手术任务是有效的.
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