Learning Human Ergonomic Preferences for Handovers
Learning Human Ergonomic Preferences for Handovers
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DOI：
10.1109/icra.2018.8461216
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Aaron M. Bestick;Ravi Pandya;R. Bajcsy;A. Dragan
中科院分区：
文献类型：
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作者：
Aaron M. Bestick;Ravi Pandya;R. Bajcsy;A. Dragan
我们的目标是让人们在从机器人手中拿东西时感到身体舒适。这给机器人增加了负担，让它以一种人可以轻松拿到的方式交出物体，而不需要绷紧或扭曲他们的手臂——一种有利于人体工程学的人类抓取配置的方式。为了实现这一点，机器人需要了解是什么使一个配置或多或少符合人体工程学，即他们的人体工程学成本函数。在这项工作中，我们将学习一个人的人体工程学成本作为一个在线估计问题。机器人可以通过将不同配置的物体交给人来隐式地向人提问，并通过观察他们选择拿取物体的方式来获得回应。我们比较了被动和主动方法在模拟和面对面用户研究中解决这个问题的性能。
Our goal is for people to be physically comfortable when taking objects from robots. This puts a burden on the robot to hand over the object in such a way that a person can easily reach it, without needing to strain or twist their arm - a way that is conducive to ergonomic human grasping configurations. To achieve this, the robot needs to understand what makes a configuration more or less ergonomic to the person, i.e. their ergonomic cost function. In this work, we formulate learning a person's ergonomic cost as an online estimation problem. The robot can implicitly make queries to the person by handing them objects in different configurations, and gets observations in response about the way they choose to take the object. We compare the performance of both passive and active approaches for solving this problem in simulation, as well as in an in-person user study.