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通过基于触觉的机器人远程操作进行学习合规的盒子内插入.

Sreekanth Kana1, Juhi Gurnani1, Vishal Ramanathan1

  • 1School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore.

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概括

这项研究使用学习从演示 (LfD) 和机器人远程操作自动化了盒子内盒子插入. 该方法利用自然盒的合规性,为精确,可适应的机器人包装解决方案提供优势.

关键词:
斯混合回归的高斯混合回归.从演示中学习.barycentric 位对应方式盒子中的盒子插入插入.符合标准的插入.触觉反是一种触觉反.人类与机器人的协作.机器人自动化机器人自动化电话操作是远程操作.

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科学领域:

  • 机器人和自动化 机器人和自动化
  • 物流和供应链管理的物流和供应链管理.
  • 机器学习用于机器人技术

背景情况:

  • 自动化符合对象插入,就像物流中的盒子内盒子任务一样,由于对象变形建模的困难而具有挑战性.
  • 从演示中学习 (LfD) 为复杂的机器人任务提供了可行的方法,这些任务很难用数学模型来模型.

研究的目的:

  • 开发和验证一个自动化系统的盒子-在-盒子插入任务使用学习从演示.
  • 为了应对对象变形建模的挑战,并使定位控制机器人能够精确插入.

主要方法:

  • 一个主-奴隶远程操作的机器人系统被用于插入任务的触觉演示.
  • 高斯混合回归被用于从演示中学习概率轨迹.
  • 用于概括插入任务并适应对象位置变化,利用了巴里中心插入.

主要成果:

  • 拟议的框架通过利用盒子的自然合规性,成功展示了自动化的盒子内盒子插入.
  • 这种方法使得即使在位置控制机器人中也可以准确插入,展示了适应性.
  • 实验验证证证实了开发的战略的概括性和可重复性.

结论:

  • 从演示中学习与概率方法和插值相结合,为复杂的机器人插入任务提供了有效的解决方案.
  • 该方法成功地利用了对象合规性,克服了自动化包装应用模拟变形的局限性.
  • 经过验证的方法为现实世界物流和包装自动化提供了强大的和可适应的解决方案.