Technological developments call for increasing perception and action
capabilities of robots. Among other skills, vision systems that can adapt to
any possible change in the working conditions are needed. Since these
conditions are unpredictable, we need benchmarks which allow to assess the
generalization and robustness capabilities of our visual recognition
algorithms. In this work we focus on robotic kitting in unconstrained
scenarios. As a first contribution, we present a new visual dataset for the
kitting task. Differently from standard object recognition datasets, we provide
images of the same objects acquired under various conditions where camera,
illumination and background are changed. This novel dataset allows for testing
the robustness of robot visual recognition algorithms to a series of different
domain shifts both in isolation and unified. Our second contribution is a novel
online adaptation algorithm for deep models, based on batch-normalization
layers, which allows to continuously adapt a model to the current working
conditions. Differently from standard domain adaptation algorithms, it does not
require any image from the target domain at training time. We benchmark the
performance of the algorithm on the proposed dataset, showing its capability to
fill the gap between the performances of a standard architecture and its
counterpart adapted offline to the given target domain.

本文提出了一种针对机器人操作中暴露的变化性的视觉基准测试方法，它包括一个新的针对机器人拼装任务的数据集，一个在线适应算法，以及对这些算法的基准测试，这将有助于评估机器人视觉识别算法的泛化能力与鲁棒性。