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.
|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|
|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
|Name||Conference on Human Factors in Computing Systems - Proceedings|
|Conference||2021 CHI Conference on Human Factors in Computing Systems: Making Waves, Combining Strengths, CHI 2021|
|Period||21/5/8 → 21/5/13|
Bibliographical noteFunding 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).
© 2021 ACM.
All Science Journal Classification (ASJC) codes
- Human-Computer Interaction
- Computer Graphics and Computer-Aided Design