Chrome Extension
WeChat Mini Program
Use on ChatGLM

Let’s Not Get Too Personal – Distance Regulation for Follow Me Robots

IFIP TC13 International Conference on Human-Computer Interaction (INTERACT)(2020)CCF C

University of Jena | Department of Psychology and Ergonomics

Cited 2|Views5
Abstract
The spatial behavior of robots working alongside humans critically influences the experience of comfort and personal space of users. The spatial behavior of service robots is especially important, as they move in close proximity to their users. To identify acceptable spatial behavior of Follow Me robots, we conducted an experimental study with 24 participants. In a within-subject design, human-robot distance was varied within the personal space (0.5 and 1.0 m) and social space (1.5 and 2.0 m). In all conditions, the robot carried a personal item of the participants. After each condition, the subjective experience of users in their interaction with the robot was assessed on the dimensions of trust, likeability, human likeness, comfort, expectation conformity, safety, and unobtrusiveness. The results show that the subjective experience of participants during the interaction with the Follow Me robot was generally more positive in the social distance conditions (1.5 and 2.0 m) than in the personal distance conditions (0.5 and 1 m). Interestingly, the following behavior was not perceived as comparable to human-human following behavior in the 0.5 and 2.0 m conditions, which were rated as either closer than human following or further away. This result, in combination with the more positive user experience in the social space conditions, illustrates that an exact transfer of interaction conventions from human-human interaction to human-robot interaction may not be feasible. And while users generally rate the interaction with Follow Me robots as positive, the following-distance of robots will need to be considered to optimize robot-behavior for user acceptance.
More
Translated text
Key words
Human robot interaction,Proxemics,Follow Me robots
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Related Papers
2000

被引用35 | 浏览

Edward T. Hall, Ray L. Birdwhistell, Bernhard Bock,Paul Bohannan,A. Richard Diebold,, Marshall Durbin,Munro S. Edmonson, J. L. Fischer,Dell Hymes,Solon T. Kimball,Weston La Barre, J. E. McClellan,
1968

被引用507 | 浏览

Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
GPU is busy, summary generation fails
Rerequest