首页> 外文会议>2012 IEEE/SICE International Symposium on System Integration. >A model to output optimal degrees of emphasis for teaching motion according to initial performance of human-learners-an empirically obtained model for robotic motion coaching system
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A model to output optimal degrees of emphasis for teaching motion according to initial performance of human-learners-an empirically obtained model for robotic motion coaching system

机译:根据人类学习者的初始表现输出最佳运动教学重点强调程度的模型-通过经验获得的机器人运动教练系统模型

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摘要

Slight differences between a learning target motion and motions performed by learners are not easy for learners to recognize, when some adjustments are needed by learners for learning motions. To assist human-learners recognize the slight differences in motions, not only symbolic expressions but also motion display plays very important role. In the previous work, we proposed a motion coaching system that dynamically modify displaying motions according to performance of human-learners. The dynamically modified displaying motions are such that they emphasize a learning target motion in direction to supplement missing elements of performed motion by learners compared to a learning target motion. However, degree of emphasis of the dynamically modified motion are given by an preliminary experiment. The robotic coaching system should be able to make decision for the degree of emphasis dynamically, in order to assist human-learners to recognize slight differences between a learning target motion and performances of human-learners. Thus, in this paper, we conduct experiments where the robotic system coaches humans motions with many different degree of emphasis and record resulting performances of human-learners for each cases. Analysis of experimental results and obtained model suggest that an optimal degree of emphasis for dynamically modified displaying motions can be decided according to initial performance of human-learners.
机译:当学习者需要一些调整来学习运动时,学习目标运动与学习者执行的运动之间的细微差异对于学习者来说并不容易。为了帮助学习者认识到运动中的细微差别,不仅符号表达而且运动显示都起着非常重要的作用。在先前的工作中,我们提出了一种运动教练系统,该系统可以根据人类学习者的表现动态修改显示的运动。动态修改的显示动作使得与学习目标动作相比,它们在方向上强调学习目标动作以补充学习者执行的动作的缺失元素。然而,通过初步实验给出了动态修改的运动的强调程度。机器人教练系统应该能够动态地确定强调的程度,以帮助人类学习者认识到学习目标的动作和人类学习者之间的细微差别。因此,在本文中,我们进行了实验,其中机器人系统以许多不同的重点来指导人类运动,并记录了每种情况下人类学习者的表现。对实验结果和所获得模型的分析表明,可以根据人类学习者的初始表现来确定动态修改的显示动作的最佳强调程度。

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