TY - GEN
T1 - Advantages of Multimodal versus Verbal-Only Robot-to-Human Communication with an Anthropomorphic Robotic Mock Driver
AU - Schreiter, Tim
AU - Morillo-Mendez, Lucas
AU - Chadalavada, Ravi T.
AU - Rudenko, Andrey
AU - Billing, Erik
AU - Magnusson, Martin
AU - Arras, Kai O.
AU - Lilienthal, Achim J.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Robots are increasingly used in shared environments with humans, making effective communication a necessity for successful human-robot interaction. In our work, we study a crucial component: active communication of robot intent. Here, we present an anthropomorphic solution where a humanoid robot communicates the intent of its host robot acting as an 'Anthropomorphic Robotic Mock Driver' (ARMoD). We evaluate this approach in two experiments in which participants work alongside a mobile robot on various tasks, while the ARMoD communicates a need for human attention, when required, or gives instructions to collaborate on a joint task. The experiments feature two interaction styles of the ARMoD: a verbal-only mode using only speech and a multimodal mode, additionally including robotic gaze and pointing gestures to support communication and register intent in space. Our results show that the multimodal interaction style, including head movements and eye gaze as well as pointing gestures, leads to more natural fixation behavior. Participants naturally identified and fixated longer on the areas relevant for intent communication, and reacted faster to instructions in collaborative tasks. Our research further indicates that the ARMoD intent communication improves engagement and social interaction with mobile robots in workplace settings.
AB - Robots are increasingly used in shared environments with humans, making effective communication a necessity for successful human-robot interaction. In our work, we study a crucial component: active communication of robot intent. Here, we present an anthropomorphic solution where a humanoid robot communicates the intent of its host robot acting as an 'Anthropomorphic Robotic Mock Driver' (ARMoD). We evaluate this approach in two experiments in which participants work alongside a mobile robot on various tasks, while the ARMoD communicates a need for human attention, when required, or gives instructions to collaborate on a joint task. The experiments feature two interaction styles of the ARMoD: a verbal-only mode using only speech and a multimodal mode, additionally including robotic gaze and pointing gestures to support communication and register intent in space. Our results show that the multimodal interaction style, including head movements and eye gaze as well as pointing gestures, leads to more natural fixation behavior. Participants naturally identified and fixated longer on the areas relevant for intent communication, and reacted faster to instructions in collaborative tasks. Our research further indicates that the ARMoD intent communication improves engagement and social interaction with mobile robots in workplace settings.
UR - http://www.scopus.com/inward/record.url?scp=85186997577&partnerID=8YFLogxK
U2 - 10.1109/RO-MAN57019.2023.10309629
DO - 10.1109/RO-MAN57019.2023.10309629
M3 - Conference contribution
AN - SCOPUS:85186997577
T3 - IEEE International Workshop on Robot and Human Communication, RO-MAN
SP - 293
EP - 300
BT - 2023 32nd IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2023
PB - IEEE Computer Society
T2 - 32nd IEEE International Conference on Robot and Human Interactive Communication, RO-MAN 2023
Y2 - 28 August 2023 through 31 August 2023
ER -