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Velocity-curvature patterns limit human-robot physical interaction

机译:速度-曲率模式限制了人机交互

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摘要

Physical human-robot collaboration is becoming more common, both in industrial and service robotics. Cooperative execution of a task requires intuitive and efficient interaction between both actors. For humans, this means being able to predict and adapt to robot movements. Given that natural human movement exhibits several robust features, we examined whether human-robot physical interaction is facilitated when these features are considered in robot control. The present study investigated how humans adapt to biological and non-biological velocity patterns in robot movements. Participants held the end-effector of a robot that traced an elliptic path with either biological (two-thirds power law) or non-biological velocity profiles. Participants were instructed to minimize the force applied on the robot end-effector. Results showed that the applied force was significantly lower when the robot moved with a biological velocity pattern. With extensive practice and enhanced feedback, participants were able to decrease their force when following a non-biological velocity pattern, but never reached forces below those obtained with the 2/3 power law profile. These results suggest that some robust features observed in natural human movements are also a strong preference in guided movements. Therefore, such features should be considered in human-robot physical collaboration.
机译:物理人机协作在工业和服务机器人技术中变得越来越普遍。协作执行任务需要两个参与者之间进行直观而有效的交互。对于人类而言,这意味着能够预测并适应机器人的运动。鉴于自然的人体运动表现出几个强大的功能,我们研究了在机器人控制中考虑这些功能时是否促进了人机交互。本研究调查了人类如何适应机器人运动中的生物和非生物速度模式。参与者手持机器人的末端执行器,该机器人利用生物(三分之二的幂定律)或非生物的速度曲线描绘了一条椭圆形路径。指示参与者最小化施加在机器人末端执行器上的力。结果表明,当机器人以生物速度模式移动时,施加的力明显较低。通过广泛的实践和增强的反馈,参与者能够遵循非生物的速度模式降低自己的力量,但从未达到低于2/3幂律分布所获得的力量。这些结果表明,在自然的人体运动中观察到的一些鲁棒性特征也是引导运动中的强烈偏好。因此,应在人机物理协作中考虑此类功能。

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