  Object detection plays a deep role in visual systems by identifying instances
for downstream algorithms. In industrial scenarios, however, a slight change in
manufacturing systems would lead to costly data re-collection and human
annotation processes to re-train models. Existing solutions such as
semi-supervised and few-shot methods either rely on numerous human annotations
or suffer low performance. In this work, we explore a novel object detector
based on interactive perception (ODIP), which can be adapted to novel domains
in an automated manner. By interacting with a grasping system, ODIP accumulates
visual observations of novel objects, learning to identify previously unseen
instances without human-annotated data. Extensive experiments show ODIP
outperforms both the generic object detector and state-of-the-art few-shot
object detector fine-tuned in traditional manners. A demo video is provided to
further illustrate the idea.
