  Stream-based active learning (AL) is an efficient training data collection
method, and it is used to reduce human annotation cost required in machine
learning. However, it is difficult to say that the human cost is low enough
because most previous studies have assumed that an oracle is a human with
domain knowledge. In this study, we propose a method to replace a part of the
oracle's work in stream-based AL by self-training with weak labeling using a
robot arm. A camera attached to a robot arm takes a series of image data
related to a streamed object, which should have the same label. We use this
information as a weak label to connect a pseudo-label (estimated class label)
and a target instance. Our method selects two data from a series of image data;
high confidence data for correcting pseudo-labels and low confidence data for
improving the performance of the classifier. We paired a pseudo-label provided
to high confidence data with a target instance (low confidence data). By using
this technique, we mitigate the inefficiency in self-training, that is,
difficulty in creating pseudo-labeled training data with a high impact on the
target classifier. In the experiments, we employed the proposed method in the
classification task of objects on a belt conveyor. We evaluated the performance
against human cost on multiple scenarios considering the temporal variation of
data. The proposed method achieves the same or better performance as the
conventional methods while reducing human cost.
