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Robot programming by non-experts: Intuitiveness and robustness of One-Shot robot programming

Eric M. Orendt, Myriel Fichtner, Dominik Henrich

发表年份
2016
引用次数
45

摘要

General purpose robots are established tools in a variety of industrial applications. Current household robots, however, are limited in capabilities and purpose. One reason for this is the programming effort required to utilize a general purpose robot. A popular strategy to reduce programming effort is Programming by Demonstration (PbD). Usually PbD requires several tedious demonstrations to teach a robot some behavior or skill. Notably, there are just few approaches where a single demonstration suffices. Such One-Shot approaches reduce programming effort even further and allow non-experts to quickly and intuitively program a general purpose robot. In this work we evaluate intuitiveness and robustness (i.e. the dependability when executing non-expert generated programs) of a One-Shot PbD system. To this end, we carried out a user study with 34 participants. Our participants used kinesthetic programming to complete various tasks with a light-weight robot. We instructed users with standard methods, either by speech, by graphical tutorial or by video. During the study, structured questionnaires and an observation sheet collected user impressions: The effectiveness when solving a task, the efficiency and (mental) effort during programming, the attitude towards the robot, and finally the satisfaction with the entire system. User ratings on all aspects confirm the high intuitiveness of the investigated system. In contrast to other approaches, the study did not depend on a specific instructional material to achieve high intuitiveness ratings. Overall, our findings support the advantages of a One-Shot programming system and give important insights into general requirements for robot programming by non-experts. We conclude that the investigated One-Shot PbD approach forms a vital tool for the use of robots in private households.

关键词

Computer scienceProgramming by demonstrationRobotRobustness (evolution)Human–computer interactionArtificial intelligenceKinesthetic learning

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