As robots increasingly take part in daily living activities, humans will have to interact with them in domestic and other human-oriented environments. We can expect that domestic robots will exhibit occasional mechanical, programming or functional errors, as occur with other electrical consumer devices. For example, these errors could include software errors, dropping objects due to gripper malfunctions, picking up the wrong object or showing faulty navigational skills due to unclear camera images or noisy laser scanner data respectively. It is therefore important for a domestic robot to have acceptable interactive behaviour when exhibiting and recovering from an error situation. As a first step, the current study investigated human users’ perceptions of the severity of various categories of potential errors that are likely to be exhibited by a domestic robot. We conducted a questionnaire-based study, where participants rated 20 different scenarios in which a domestic robot made an error. The potential errors were rated by participants by severity. Our findings indicate that people perceptions of the magnitude of the errors presented in the questionnaire were consistent. We did not find any significant differences in users’ ratings due to age and gender. We clearly identified scenarios that were rated by participants as having limited consequences (“small” errors) and that were rated as having severe consequences (“big” errors). Future work will use these two sets of consistently rated robot error scenarios as baseline scenarios to perform studies with repeated interactions investigating human perceptions of robot tasks and error severity.

Human Perceptions of the Severity of Domestic Robot Errors / Rossi, A.; Dautenhahn, K.; Koay, K. L.; Walters, M. L.. - 10652:(2017), pp. 647-656. (Intervento presentato al convegno 9th International Conference on Social Robotics, ICSR 2017 tenutosi a jpn nel 2017) [10.1007/978-3-319-70022-9_64].

Human Perceptions of the Severity of Domestic Robot Errors

Rossi A.;
2017

Abstract

As robots increasingly take part in daily living activities, humans will have to interact with them in domestic and other human-oriented environments. We can expect that domestic robots will exhibit occasional mechanical, programming or functional errors, as occur with other electrical consumer devices. For example, these errors could include software errors, dropping objects due to gripper malfunctions, picking up the wrong object or showing faulty navigational skills due to unclear camera images or noisy laser scanner data respectively. It is therefore important for a domestic robot to have acceptable interactive behaviour when exhibiting and recovering from an error situation. As a first step, the current study investigated human users’ perceptions of the severity of various categories of potential errors that are likely to be exhibited by a domestic robot. We conducted a questionnaire-based study, where participants rated 20 different scenarios in which a domestic robot made an error. The potential errors were rated by participants by severity. Our findings indicate that people perceptions of the magnitude of the errors presented in the questionnaire were consistent. We did not find any significant differences in users’ ratings due to age and gender. We clearly identified scenarios that were rated by participants as having limited consequences (“small” errors) and that were rated as having severe consequences (“big” errors). Future work will use these two sets of consistently rated robot error scenarios as baseline scenarios to perform studies with repeated interactions investigating human perceptions of robot tasks and error severity.
2017
Human Perceptions of the Severity of Domestic Robot Errors / Rossi, A.; Dautenhahn, K.; Koay, K. L.; Walters, M. L.. - 10652:(2017), pp. 647-656. (Intervento presentato al convegno 9th International Conference on Social Robotics, ICSR 2017 tenutosi a jpn nel 2017) [10.1007/978-3-319-70022-9_64].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/905164
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