Optimal action-based or user prediction-based haptic guidance: Can you do even better?

Hee Seung Moon; Jiwon Seo

doi:10.1145/3411764.3445115

Optimal action-based or user prediction-based haptic guidance: Can you do even better?

Hee Seung Moon, Jiwon Seo

School of Integrated Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Citations (Scopus)

Abstract

The recently advanced robotics technology enables robots to assist users in their daily lives. Haptic guidance (HG) improves users' task performance through physical interaction between robots and users. It can be classifed into optimal action-based HG (OAHG), which assists users with an optimal action, and user prediction-based HG (UPHG), which assists users with their next predicted action. This study aims to understand the diference between OAHG and UPHG and propose a combined HG (CombHG) that achieves optimal performance by complementing each HG type, which has important implications for HG design. We propose implementation methods for each HG type using deep learning-based approaches. A user study (n=20) in a haptic task environment indicated that UPHG induces better subjective evaluations, such as naturalness and comfort, thanOAHG. In addition, the CombHG thatwe proposed further decreases the disagreement between the user intention and HG, without reducing the objective and subjective scores.

Original language	English
Title of host publication	CHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Subtitle of host publication	Making Waves, Combining Strengths
Publisher	Association for Computing Machinery (ACM)
ISBN (Electronic)	9781450380966
DOIs	https://doi.org/10.1145/3411764.3445115
Publication status	Published - 2021 May 6
Event	2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021 - Virtual, Online, Japan Duration: 2021 May 8 → 2021 May 13

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings

Conference

Conference	2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021
Country/Territory	Japan
City	Virtual, Online
Period	21/5/8 → 21/5/13

Bibliographical note

Funding Information:
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07043580).

Publisher Copyright:
© 2021 ACM.

All Science Journal Classification (ASJC) codes

Software
Human-Computer Interaction
Computer Graphics and Computer-Aided Design

Access to Document

10.1145/3411764.3445115

Cite this

Moon, H. S., & Seo, J. (2021). Optimal action-based or user prediction-based haptic guidance: Can you do even better? In CHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery (ACM). https://doi.org/10.1145/3411764.3445115

@inproceedings{969de8b1192142359e851a893a42c227,

title = "Optimal action-based or user prediction-based haptic guidance: Can you do even better?",

abstract = "The recently advanced robotics technology enables robots to assist users in their daily lives. Haptic guidance (HG) improves users' task performance through physical interaction between robots and users. It can be classifed into optimal action-based HG (OAHG), which assists users with an optimal action, and user prediction-based HG (UPHG), which assists users with their next predicted action. This study aims to understand the diference between OAHG and UPHG and propose a combined HG (CombHG) that achieves optimal performance by complementing each HG type, which has important implications for HG design. We propose implementation methods for each HG type using deep learning-based approaches. A user study (n=20) in a haptic task environment indicated that UPHG induces better subjective evaluations, such as naturalness and comfort, thanOAHG. In addition, the CombHG thatwe proposed further decreases the disagreement between the user intention and HG, without reducing the objective and subjective scores.",

author = "Moon, {Hee Seung} and Jiwon Seo",

note = "Funding Information: This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07043580). Publisher Copyright: {\textcopyright} 2021 ACM.; 2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021 ; Conference date: 08-05-2021 Through 13-05-2021",

year = "2021",

month = may,

day = "6",

doi = "10.1145/3411764.3445115",

language = "English",

series = "Conference on Human Factors in Computing Systems - Proceedings",

publisher = "Association for Computing Machinery (ACM)",

booktitle = "CHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems",

address = "United States",

}

Moon, HS & Seo, J 2021, Optimal action-based or user prediction-based haptic guidance: Can you do even better? in CHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths. Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery (ACM), 2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021, Virtual, Online, Japan, 21/5/8. https://doi.org/10.1145/3411764.3445115

Optimal action-based or user prediction-based haptic guidance: Can you do even better? / Moon, Hee Seung; Seo, Jiwon.
CHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths. Association for Computing Machinery (ACM), 2021. (Conference on Human Factors in Computing Systems - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Optimal action-based or user prediction-based haptic guidance

T2 - 2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021

AU - Moon, Hee Seung

AU - Seo, Jiwon

N1 - Funding Information: This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07043580). Publisher Copyright: © 2021 ACM.

PY - 2021/5/6

Y1 - 2021/5/6

N2 - The recently advanced robotics technology enables robots to assist users in their daily lives. Haptic guidance (HG) improves users' task performance through physical interaction between robots and users. It can be classifed into optimal action-based HG (OAHG), which assists users with an optimal action, and user prediction-based HG (UPHG), which assists users with their next predicted action. This study aims to understand the diference between OAHG and UPHG and propose a combined HG (CombHG) that achieves optimal performance by complementing each HG type, which has important implications for HG design. We propose implementation methods for each HG type using deep learning-based approaches. A user study (n=20) in a haptic task environment indicated that UPHG induces better subjective evaluations, such as naturalness and comfort, thanOAHG. In addition, the CombHG thatwe proposed further decreases the disagreement between the user intention and HG, without reducing the objective and subjective scores.

AB - The recently advanced robotics technology enables robots to assist users in their daily lives. Haptic guidance (HG) improves users' task performance through physical interaction between robots and users. It can be classifed into optimal action-based HG (OAHG), which assists users with an optimal action, and user prediction-based HG (UPHG), which assists users with their next predicted action. This study aims to understand the diference between OAHG and UPHG and propose a combined HG (CombHG) that achieves optimal performance by complementing each HG type, which has important implications for HG design. We propose implementation methods for each HG type using deep learning-based approaches. A user study (n=20) in a haptic task environment indicated that UPHG induces better subjective evaluations, such as naturalness and comfort, thanOAHG. In addition, the CombHG thatwe proposed further decreases the disagreement between the user intention and HG, without reducing the objective and subjective scores.

UR - http://www.scopus.com/inward/record.url?scp=85124999558&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85124999558&partnerID=8YFLogxK

U2 - 10.1145/3411764.3445115

DO - 10.1145/3411764.3445115

M3 - Conference contribution

AN - SCOPUS:85124999558

T3 - Conference on Human Factors in Computing Systems - Proceedings

BT - CHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

PB - Association for Computing Machinery (ACM)

Y2 - 8 May 2021 through 13 May 2021

ER -

Moon HS, Seo J. Optimal action-based or user prediction-based haptic guidance: Can you do even better? In CHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths. Association for Computing Machinery (ACM). 2021. (Conference on Human Factors in Computing Systems - Proceedings). doi: 10.1145/3411764.3445115

Optimal action-based or user prediction-based haptic guidance: Can you do even better?

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this