  This paper describes a method of online refinement of a scene recognition
model for robot navigation considering traversable plants, flexible plant parts
which a robot can push aside while moving. In scene recognition systems that
consider traversable plants growing out to the paths, misclassification may
lead the robot to getting stuck due to the traversable plants recognized as
obstacles. Yet, misclassification is inevitable in any estimation methods. In
this work, we propose a framework that allows for refining a semantic
segmentation model on the fly during the robot's operation. We introduce a
few-shot segmentation based on weight imprinting for online model refinement
without fine-tuning. Training data are collected via observation of a human's
interaction with the plant parts. We propose novel robust weight imprinting to
mitigate the effect of noise included in the masks generated by the
interaction. The proposed method was evaluated through experiments using
real-world data and shown to outperform an ordinary weight imprinting and
provide competitive results to fine-tuning with model distillation while
requiring less computational cost.
