Kitting in the Wild through Online Domain Adaptation
Massimiliano Mancini, Hakan Karaoguz, Elisa Ricci, Patric Jensfelt and, Barbara Caputo

TL;DR
This paper introduces a new dataset for robotic kitting under varied conditions and proposes an online adaptation method that improves visual recognition robustness without requiring target domain data during training.
Contribution
It presents a novel dataset for evaluating robustness of visual recognition in robotic kitting and an online adaptation algorithm based on batch-normalization that operates without target domain data.
Findings
The dataset enables testing of domain shift robustness in robotic vision.
The online adaptation method improves recognition performance under changing conditions.
The algorithm narrows the gap between standard and offline adapted models.
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…
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