Title: Kitting in the Wild through Online Domain Adaptation,
Abstract: 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.