  We introduce a novel approach for scalable domain adaptation in cloud
robotics scenarios where robots rely on third-party AI inference services
powered by large pre-trained deep neural networks. Our method is based on a
downstream proposal-refinement stage running locally on the robots, exploiting
a new lightweight DNN architecture, R2SNet. This architecture aims to mitigate
performance degradation from domain shifts by adapting the object detection
process to the target environment, focusing on relabeling, rescoring, and
suppression of bounding-box proposals. Our method allows for local execution on
robots, addressing the scalability challenges of domain adaptation without
incurring significant computational costs. Real-world results on mobile service
robots performing door detection show the effectiveness of the proposed method
in achieving scalable domain adaptation.
