  Deep Learning (DL) based methods for object detection achieve remarkable
performance at the cost of computationally expensive training and extensive
data labeling. Robots embodiment can be exploited to mitigate this burden by
acquiring automatically annotated training data via a natural interaction with
a human showing the object of interest, handheld. However, learning solely from
this data may introduce biases (the so-called domain shift), and prevents
adaptation to novel tasks. While Weakly-supervised Learning (WSL) offers a
well-established set of techniques to cope with these problems in
general-purpose Computer Vision, its adoption in challenging robotic domains is
still at a preliminary stage. In this work, we target the scenario of a robot
trained in a teacher-learner setting to detect handheld objects. The aim is to
improve detection performance in different settings by letting the robot
explore the environment with a limited human labeling budget. We compare
several techniques for WSL in detection pipelines to reduce model re-training
costs without compromising accuracy, proposing solutions which target the
considered robotic scenario. We show that the robot can improve adaptation to
novel domains, either by interacting with a human teacher (Active Learning) or
with an autonomous supervision (Semi-supervised Learning). We integrate our
strategies into an on-line detection method, achieving efficient model update
capabilities with few labels. We experimentally benchmark our method on
challenging robotic object detection tasks under domain shift.
