Physical human-robot interaction

Mutual learning and adaptation

Shuhei Ikemoto, Hani Ben Amor, Takashi Minato, Bernhard Jung, Hiroshi Ishiguro

Research output: Contribution to journalArticle

68 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Article number6161710
Pages (from-to)24-35
Number of pages12
JournalIEEE Robotics and Automation Magazine
Volume19
Issue number4
DOIs
StatePublished - 2012
Externally publishedYes

Fingerprint

Human robot interaction
Robots
Learning algorithms
Learning systems

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications

Cite this

Physical human-robot interaction : Mutual learning and adaptation. / Ikemoto, Shuhei; Ben Amor, Hani; Minato, Takashi; Jung, Bernhard; Ishiguro, Hiroshi.

In: IEEE Robotics and Automation Magazine, Vol. 19, No. 4, 6161710, 2012, p. 24-35.

Research output: Contribution to journalArticle

Ikemoto, Shuhei ; Ben Amor, Hani ; Minato, Takashi ; Jung, Bernhard ; Ishiguro, Hiroshi. / Physical human-robot interaction : Mutual learning and adaptation. In: IEEE Robotics and Automation Magazine. 2012 ; Vol. 19, No. 4. pp. 24-35.
@article{fcdc151999444a379fd86ad7f44f8c44,
title = "Physical human-robot interaction: Mutual learning and adaptation",
abstract = "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.",
author = "Shuhei Ikemoto and {Ben Amor}, Hani and Takashi Minato and Bernhard Jung and Hiroshi Ishiguro",
year = "2012",
doi = "10.1109/MRA.2011.2181676",
language = "English (US)",
volume = "19",
pages = "24--35",
journal = "IEEE Robotics and Automation Magazine",
issn = "1070-9932",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "4",

}

TY - JOUR

T1 - Physical human-robot interaction

T2 - Mutual learning and adaptation

AU - Ikemoto, Shuhei

AU - Ben Amor, Hani

AU - Minato, Takashi

AU - Jung, Bernhard

AU - Ishiguro, Hiroshi

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84871379826&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84871379826&partnerID=8YFLogxK

U2 - 10.1109/MRA.2011.2181676

DO - 10.1109/MRA.2011.2181676

M3 - Article

VL - 19

SP - 24

EP - 35

JO - IEEE Robotics and Automation Magazine

JF - IEEE Robotics and Automation Magazine

SN - 1070-9932

IS - 4

M1 - 6161710

ER -