This paper assesses how the accuracy in user’s answers influence the learning of a social robot when it is trained to recognize poses
using Active Learning. We study the performance of a robot trained to
recognize the same poses actively and passively and we show that, sometimes, the user might give simplistic answers producing a negative impact
on the robot’s learning. To reduce this effect, we provide a method based
on lowering the trust in the user’s responses. We conduct experiments
with 24 users, indicating that our method maintains the benefits of AL
even when the user answers are not accurate. With this method the robot
incorporates domain knowledge from the users, mitigating the impact of
low quality answers.
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