Learning novel objects during robot exploration via human-informed few-shot detection

NSH 1109

Abstract: Autonomous mobile robots exploring in unfamiliar environments often need to detect target objects during exploration. Most prevalent approach is to use conventional object detection models, by training the object detector on large abundant image-annotation dataset, with a fixed and predefined categories of objects, and in advance of robot deployment. However, it lacks the capability [...]