Toward Human-in-the-Loop Construction Robotics: Understanding Workers’ Response through Trust Measurement during Human-Robot Collaboration

人在回路中 机器人学 人机交互 人工智能 机器人 背景(考古学) 计算机科学 人机交互 感知 知识管理 心理学 古生物学 神经科学 生物
作者
Shayan Shayesteh,Houtan Jebelli
标识
DOI:10.1061/9780784483961.066
摘要

The use of construction robots to facilitate labor-intensive tasks is increasing in many jobsites. However, human intelligence and expertise are yet crucial to support such robots in accomplishing their tasks. Therefore, it is essential that human-robot teams entail a human-in-the-loop approach for integrating humans into the system. To reliably design such systems, it is critical to understand workers' confidence in such models, particularly in hazardous and complex construction jobsites. In this context, assessing workers' trust can shed light on the efficient adoption of robots. To that end, this study investigates the impact of the human-in-the-loop approach on human-robot trust. Accordingly, a comparative experiment was conducted in an immersive virtual environment in which participants performed brick-laying tasks in collaboration with robots through two different approaches: a human-in-the-loop approach and a human-out-the-loop approach. The participants' trust in the robot was measured using the Trust Perception Scale-HRI. The Wilcoxon signed-rank test revealed that the human-in-the-loop approach could lead to significantly higher levels of trust. The findings of this study provide insights into the effective adoption of robots in construction sites. Thus, this study can open new doors to facilitating the imminent paradigm shift toward the human-in-the-loop approach in construction robotics.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
yjh123应助yu采纳,获得50
刚刚
刚刚
大模型应助落羽采纳,获得10
刚刚
liyuan发布了新的文献求助10
刚刚
SciGPT应助WWW采纳,获得10
刚刚
刚刚
肉松发布了新的文献求助10
1秒前
JamesPei应助奈何采纳,获得10
1秒前
崴Jio辣子面完成签到,获得积分10
1秒前
打打应助懵懂的柚子采纳,获得10
1秒前
东方元语应助Hank采纳,获得20
1秒前
在水一方应助钟什么海采纳,获得10
2秒前
Li chun sheng发布了新的文献求助10
2秒前
2秒前
lxl发布了新的文献求助10
3秒前
感动葵阴发布了新的文献求助10
3秒前
王富贵发布了新的文献求助10
3秒前
4秒前
Orange应助zz采纳,获得10
4秒前
plusweng完成签到 ,获得积分10
4秒前
Steve发布了新的文献求助10
5秒前
ccc发布了新的文献求助10
5秒前
852应助Lx采纳,获得10
5秒前
5秒前
6秒前
6秒前
情怀应助cy123采纳,获得10
7秒前
7秒前
CipherSage应助焱垚采纳,获得10
8秒前
斯文败类应助睡个大觉采纳,获得10
8秒前
9秒前
彭于晏应助Nie采纳,获得100
9秒前
9秒前
9秒前
saturn完成签到,获得积分10
9秒前
9秒前
喜欢发呆的怪物完成签到,获得积分10
9秒前
10秒前
10秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Römisch-Germanische Forschungen 1000
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The fast track to determining transfer functions of linear circuits: The student guide 500
The Analytical and Numerical Solution of Electric and Magnetic Fields 500
Green Fire Retardants for Polymeric Materials 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7615494
求助须知:如何正确求助?哪些是违规求助? 9190800
关于积分的说明 19693491
捐赠科研通 7188075
什么是DOI,文献DOI怎么找? 3271364
关于科研通互助平台的介绍 2434568
邀请新用户注册赠送积分活动 2266438