TY - GEN
T1 - From Raw Signals to Human Skills Level in Physical Human-Robot Collaboration for Advanced-Manufacturing Applications
AU - Blanchet, Katleen
AU - Kchir, Selma
AU - Bouzeghoub, Amel
AU - Lebec, Olivier
AU - Hède, Patrick
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Providing individualized assistance to a human when she/he is physically interacting with a robot is a challenge that necessarily entails user profiling. The identification of the human profile in advanced-manufacturing is only partially addressed in the literature, through either intrusive, or not fully transparent approaches. As on-the-job training has a negative impact on operators’ working conditions, we specifically focus on their skills, and show that they can be observed in a non-intrusive way, through a data-driven approach to extract knowledge from the internal data of the robot. To this end, we have defined useful characteristics derived from raw data, called in this paper Key Skill Indicators (KSI), and have devised a user’s skills model based on expert knowledge. Experiments from real cases show promising results, especially that our approach is able to distinguish more finely a skilled human from a novice, and that the latter would benefit from assistance regarding specific skills.
AB - Providing individualized assistance to a human when she/he is physically interacting with a robot is a challenge that necessarily entails user profiling. The identification of the human profile in advanced-manufacturing is only partially addressed in the literature, through either intrusive, or not fully transparent approaches. As on-the-job training has a negative impact on operators’ working conditions, we specifically focus on their skills, and show that they can be observed in a non-intrusive way, through a data-driven approach to extract knowledge from the internal data of the robot. To this end, we have defined useful characteristics derived from raw data, called in this paper Key Skill Indicators (KSI), and have devised a user’s skills model based on expert knowledge. Experiments from real cases show promising results, especially that our approach is able to distinguish more finely a skilled human from a novice, and that the latter would benefit from assistance regarding specific skills.
KW - Advanced-Manufacturing
KW - Expertise
KW - Human-robot collaboration
KW - Non-intrusive method
KW - Skill level
KW - User profiling
U2 - 10.1007/978-3-030-36711-4_47
DO - 10.1007/978-3-030-36711-4_47
M3 - Conference contribution
AN - SCOPUS:85076911844
SN - 9783030367107
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 554
EP - 565
BT - Neural Information Processing - 26th International Conference, ICONIP 2019, Proceedings
A2 - Gedeon, Tom
A2 - Wong, Kok Wai
A2 - Lee, Minho
PB - Springer
T2 - 26th International Conference on Neural Information Processing, ICONIP 2019
Y2 - 12 December 2019 through 15 December 2019
ER -