Physical Human-Robot Interaction Mutual Learning and Adaptation

被引：82

作者：

Ikemoto, Shuhei ^{[1
]}

Ben Amor, Heni ^{[2
]}

Minato, Takashi ^{[3
]}

Jung, Bernhard ^{[4
]}

Ishiguro, Hiroshi ^{[5
]}

机构：

[1] Osaka Univ, Dept Multimedia Engn, Suita, Osaka 565, Japan

[2] Tech Univ Darmstadt, Intelligent Autonomous Syst Grp, Darmstadt, Germany

[3] ATR Hiroshi Ishiguro Lab, Kyoto, Japan

[4] Tech Univ Bergakad Freiberg, Virtual Real & Multimedia Grp, Freiberg, Germany

[5] Osaka Univ, Dept Syst Innovat, Suita, Osaka 565, Japan

来源：

IEEE ROBOTICS & AUTOMATION MAGAZINE | 2012年 / 19卷 / 04期

关键词：

Human-robot interaction; Learning systems; Behavioral science; Service robots; Machine learning; BODY;

D O I：

10.1109/MRA.2011.2181676

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Close physical interaction between robots and humans is a particularly challenging aspect of robot development. For successful interaction and cooperation, the robot must have the ability to adapt its behavior to the human counterpart. Based on our earlier work, we present and evaluate a computationally efficient machine learning algorithm that is well suited for such close-contact interaction scenarios. We show that this algorithm helps to improve the quality of the interaction between a robot and a human caregiver. To this end, we present two human-in-the-loop learning scenarios that are inspired by human parenting behavior, namely, an assisted standing-up task and an assisted walking task. © 1994-2011 IEEE.

引用

页码：24 / 35

页数：12

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