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

Hee Seung Moon, Jiwon Seo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish
Title of host publicationCHI 2021 - Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems
Subtitle of host publicationMaking Waves, Combining Strengths
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450380966
DOIs
Publication statusPublished - 2021 May 6
Event2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021 - Virtual, Online, Japan
Duration: 2021 May 82021 May 13

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021
Country/TerritoryJapan
CityVirtual, Online
Period21/5/821/5/13

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

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

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