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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Training Persons with Spinal Cord Injury to Ambulate Using a Powered Exoskeleton
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通过模拟学习提供无实验外骨架辅助

Shuzhen Luo1,2, Menghan Jiang1, Sainan Zhang1

  • 1Lab of Biomechatronics and Intelligent Robotics, Department of Mechanical and Aerospace Engineering, North Carolina State University, Raleigh, NC, USA.

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概括

这项研究引入了一种无实验的方法,使用模拟来开发多功能外骨控制策略. 这种方法减少了行走和跑步等活动的代谢成本,为更广泛的辅助机器人采用铺平了道路.

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科学领域:

  • 机器人技术
  • 生物力学
  • 机器学习

背景情况:

  • 外骨架可以增强人类的运动能力,但广泛的人体测试和手动控制设计阻碍了其发展.
  • 弥合机器人控制的模拟与现实差距是一个重大挑战.

研究的目的:

  • 在模拟中开发一种无实验的方法来学习多功能外骨控制策略.
  • 帮助机器人的快速发展和广泛采用.

主要方法:

  • 使用学习模拟框架与动态意识的肌肉骨和外骨模型.
  • 使用数据驱动的强化学习来培养没有人体实验的控制政策.
  • 在一个定制的部外骨架上部署了学习控制器.

主要成果:

  • 达到显著的新陈代谢率下降:步行24. 3%,跑步13. 1%,爬楼梯15. 4%.
  • 证明可以适应不同活动的多功能控制政策.
  • 已经成功地弥合了模拟与现实之间的差距.

结论:

  • 拟议的框架为开发辅助机器人提供了可通用和可扩展的战略.
  • 这种方法可以加速为有能力和行动障碍的人创造机器人解决方案.
  • 消除了对外骨控制发展的漫长人体试验的需要.