  While learning from synthetic training data has recently gained an increased
attention, in real-world robotic applications, there are still performance
deficiencies due to the so-called Sim-to-Real gap. In practice, this gap is
hard to resolve with only synthetic data. Therefore, we focus on an efficient
acquisition of real data within a Sim-to-Real learning pipeline. Concretely, we
employ deep Bayesian active learning to minimize manual annotation efforts and
devise an autonomous learning paradigm to select the data that is considered
useful for the human expert to annotate. To achieve this, a Bayesian Neural
Network (BNN) object detector providing reliable uncertainty estimates is
adapted to infer the informativeness of the unlabeled data. Furthermore, to
cope with mis-alignments of the label distribution in uncertainty-based
sampling, we develop an effective randomized sampling strategy that performs
favorably compared to other complex alternatives. In our experiments on object
classification and detection, we show benefits of our approach and provide
evidence that labeling efforts can be reduced significantly. Finally, we
demonstrate the practical effectiveness of this idea in a grasping task on an
assistive robot.
