Robotics Privacy-Oriented Object Detection in Curious Cloud Robotics via Proposal Selection This paper addresses the privacy issues of cloud-based object detection for mobile robots. Our approach involves co-training an encoder-decoder architecture to retain only task-specific features while obfuscating sensitive information, leveraging a novel mechanism of weak loss with proposal selection. R2SNet: Scalable Domain Adaptation for Object Detection in Cloud-Based Robotic Ecosystems via Proposal Refinement R2SNet aims to mitigate performance degradation from domain shifts by adapting the object detection process to the robot's target environment, focusing on relabeling, rescoring, and suppression of bounding-box proposals. Frontier-Based Exploration for Multi-Robot Rendezvous in Communication-Restricted Unknown Environments We adapt a standard frontier-based exploration technique to integrate exploration and rendezvous into a unified strategy, with a mechanism that allows robots to re-visit previously explored regions thus enhancing rendezvous opportunities. Development and Adaptation of Robotic Vision in the Real-World: the Challenge of Door Detection We demonstrate with an extensive methodological evidence how the integration of simulation frameworks and domain adaptation techniques can create an effective development pipeline for constructing door detectors specifically tailored for mobile service robots operating in the real world.
