The effects of available complexity in verbal commands on robot imitation task performance and user satisfaction

Abstract

Programming robots with verbal commands is limited by the capabilities of the utilized natural language parser. A simple natural language parser which can understand only keywords and small phrases may be easy to use, but limited in what it can interpret and convey. Alternatively, a natural language parser which understands more complex commands can be used to convey more nuance, but can be more difficult to use and create. It is unclear when having complex verbal commands available is preferable to having only simple verbal commands available. Here we show that using natural language parsers which understand more complex commands are preferable when teaching a robot, both in terms of user preference and objective metrics such as completion time and accuracy, but only when the task is complex as well. During a preliminary wizard of oz experiment, we observed what types of phrases users use to correct the robot during a pose imitation learning task, in order to create multiple natural language parsers which allowed for different levels of complexity in given verbal feedback. In a follow up experiment, in which 24 users utilized these parsers in a similar task, the users reported finding the more complex ones to be more useful and satisfying to use. Additionally, the more complex parsers also led to a higher objective similarity between the pose that the user wanted to convey and the final attained pose by the robot. However, this last result was only found for poses which required a comparably high effort on part of the user.