  We present an end-to-end framework for fast retraining of object detection
models in human-robot-collaboration. Our Faster R-CNN based setup covers the
whole workflow of automatic image generation and labeling, model retraining
on-site as well as inference on a FPGA edge device. The intervention of a human
operator reduces to providing the new object together with its label and
starting the training process. Moreover, we present a new loss, the
intraspread-objectosphere loss, to tackle the problem of open world
recognition. Though it fails to completely solve the problem, it significantly
reduces the number of false positive detections of unknown objects.
