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Real-Time Adaptive Industrial Robots: Improving Safety And Comfort In Human-Robot Collaboration

Type
conference paper
Date Issued
2025-05-26
Author(s)
Damian Hostettler  
;
Simon Mayer  
;
Albert, Jan
;
Jenss, Kay
;
Christian Alexander Hildebrand  
DOI
10.1145/3706598.3713889
Abstract
Industrial robots become increasingly prevalent, resulting in a growing need for intuitive, comforting human-robot collaboration. We present a user-aware robotic system that adapts to operator behavior in real time while non-intrusively monitoring physiological signals to create a more responsive and empathetic environment. Our prototype dynamically adjusts robot speed and movement patterns to proxemics while measuring operator pupil dilation. Our user study compares this adaptive system to a non-adaptive counterpart, and demonstrates that the adaptive system significantly reduces both perceived and physiologically measured cognitive load while enhancing usability. Participants reported increased feelings of comfort, safety, trust, and a stronger sense of collaboration when working with the adaptive robot. This highlights the potential of integrating real-time physiological data into human-robot interaction paradigms. This novel approach creates more intuitive and collaborative industrial environments where robots effectively 'read' and respond to human cognitive states, and we feature all data and code for future use.
Language
English
Keywords
Adaptive Robot
Industrial Robot
User Study
Pupillometry
Proxemics
HSG Classification
contribution to scientific community
Refereed
Yes
Book title
Proceedings of the 2025 ACM CHI Conference on Human Factors in Computing Systems
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/122100
Subject(s)

computer science

Division(s)

ICS - Institute of Co...

File(s)
Thumbnail Image
Name

chi25b-sub3425-cam-i16.pdf

Size

10.01 MB

Format

Adobe PDF

Checksum (MD5)

a46cd8330b25ca927dcac392effe2aef

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