TY - GEN
T1 - Optometrist's Algorithm for Personalizing Robot-Human Handovers
AU - Gupte, Vivek
AU - Suissa, Dan R.
AU - Edan, Yael
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - With an increasing interest in human-robot collaboration, there is a need to develop robot behavior while keeping the human user's preferences in mind. Highly skilled human users doing delicate tasks require their robot partners to behave according to their work habits and task constraints. To achieve this, we present the use of the Optometrist's Algorithm (OA) to interactively and intuitively personalize robot-human handovers. Using this algorithm, we tune controller parameters for speed, location, and effort. We study the differences in the fluency of the handovers before and after tuning and the subjective perception of this process in a study of N = 30 non-expert users of mixed background - evaluating the OA. The users evaluate the interaction on trust, safety, and workload scales, amongst other measures. They assess our tuning process to be engaging and easy to use. Personalization leads to an increase in the fluency of the interaction. Our participants utilize the wide range of parameters ending up with their unique personalized handover.
AB - With an increasing interest in human-robot collaboration, there is a need to develop robot behavior while keeping the human user's preferences in mind. Highly skilled human users doing delicate tasks require their robot partners to behave according to their work habits and task constraints. To achieve this, we present the use of the Optometrist's Algorithm (OA) to interactively and intuitively personalize robot-human handovers. Using this algorithm, we tune controller parameters for speed, location, and effort. We study the differences in the fluency of the handovers before and after tuning and the subjective perception of this process in a study of N = 30 non-expert users of mixed background - evaluating the OA. The users evaluate the interaction on trust, safety, and workload scales, amongst other measures. They assess our tuning process to be engaging and easy to use. Personalization leads to an increase in the fluency of the interaction. Our participants utilize the wide range of parameters ending up with their unique personalized handover.
UR - http://www.scopus.com/inward/record.url?scp=85186987550&partnerID=8YFLogxK
U2 - 10.1109/RO-MAN57019.2023.10309397
DO - 10.1109/RO-MAN57019.2023.10309397
M3 - Conference contribution
AN - SCOPUS:85186987550
T3 - IEEE International Workshop on Robot and Human Communication, RO-MAN
SP - 2366
EP - 2372
BT - 2023 32nd IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2023
PB - Institute of Electrical and Electronics Engineers
T2 - 32nd IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2023
Y2 - 28 August 2023 through 31 August 2023
ER -