  Reliable perception and efficient adaptation to novel conditions are priority
skills for humanoids that function in dynamic environments. The vast
advancements in latest computer vision research, brought by deep learning
methods, are appealing for the robotics community. However, their adoption in
applied domains is not straightforward since adapting them to new tasks is
strongly demanding in terms of annotated data and optimization time.
Nevertheless, robotic platforms, and especially humanoids, present
opportunities (such as additional sensors and the chance to explore the
environment) that can be exploited to overcome these issues. In this paper, we
present a pipeline for efficiently training an object detection system on a
humanoid robot. The proposed system allows to iteratively adapt an object
detection model to novel scenarios, by exploiting: (i) a teacher-learner
pipeline, (ii) weakly supervised learning techniques to reduce the human
labeling effort and (iii) an on-line learning approach for fast model
re-training. We use the R1 humanoid robot for both testing the proposed
pipeline in a real-time application and acquire sequences of images to
benchmark the method. We made the code of the application publicly available.
