TY - GEN
T1 - Reasoning the Trust of Humans in Robots through Physiological Biometrics in Human-Robot Collaborative Contexts
AU - Guo, Tiffany
AU - Obidat, Omar
AU - Rodriguez, Laury
AU - Parron, Jesse
AU - Wang, Weitian
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - With the rapid recent growth of automation and artificial intelligence, human-robot collaboration (HRC) is playing a significant role across a variety of fields. Trust between humans and robots is an important element to enable the efficiency and success of HRC. The lack of trust of humans in robots can have critical consequences, especially in real-world applications in which humans must adapt to unfamiliar situations. In this work, we develop a novel and effective approach for robots to actively reason and respond to dynamic human emotions and trust levels during shared tasks. We implement a real-world validation experiment in the context of human-robot object hand-over, which shows the robot's ability to correctly identify and predict the human's trust levels in real-Time and assist the human accordingly in human-robot collaborative tasks. Future work on how to improve the performance of the proposed approach is also discussed.
AB - With the rapid recent growth of automation and artificial intelligence, human-robot collaboration (HRC) is playing a significant role across a variety of fields. Trust between humans and robots is an important element to enable the efficiency and success of HRC. The lack of trust of humans in robots can have critical consequences, especially in real-world applications in which humans must adapt to unfamiliar situations. In this work, we develop a novel and effective approach for robots to actively reason and respond to dynamic human emotions and trust levels during shared tasks. We implement a real-world validation experiment in the context of human-robot object hand-over, which shows the robot's ability to correctly identify and predict the human's trust levels in real-Time and assist the human accordingly in human-robot collaborative tasks. Future work on how to improve the performance of the proposed approach is also discussed.
KW - Extreme Learning Machine
KW - Robotics
KW - computer vision
KW - human-robot interaction
KW - trust
UR - http://www.scopus.com/inward/record.url?scp=85146709629&partnerID=8YFLogxK
U2 - 10.1109/URTC56832.2022.10002210
DO - 10.1109/URTC56832.2022.10002210
M3 - Conference contribution
AN - SCOPUS:85146709629
T3 - 2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022
BT - 2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022
Y2 - 30 September 2022 through 2 October 2022
ER -