As the adoption of large-scale model-based AI grows, the field of robotics is undergoing significant changes. The emergence of cloud robotics, where advanced tasks are offloaded to fog or cloud servers, is gaining attention. However, the widely used Robot Operating System (ROS) does not support communication between robot software across different networks. This paper introduces ROS Gateway, a middleware designed to improve the usability and extend the communication range of ROS in multi-network environments, which is important for processing sensor data in cloud robotics. We detail its structure, protocols, and algorithms, highlighting improvements over traditional ROS configurations. The ROS Gateway efficiently handles high-volume data from advanced sensors such as depth cameras and LiDAR, ensuring reliable transmission. Based on the rosbridge protocol and implemented in Python 3, ROS Gateway is compatible with rosbridge-based tools and runs on both x86 and ARM-based Linux environments. Our experiments show that the ROS Gateway significantly improves performance metrics such as topic rate and delay compared to standard ROS setups. We also provide predictive formulas for topic receive rates to guide the design and deployment of robotic applications using ROS Gateway, supporting performance estimation and system optimization. These enhancements are essential for developing responsive and intelligent robotic systems in dynamic environments.

An approach to identifying a caller or callers by a service robot is presented for a natural interaction with people in a home/office environment. The problem addressed specifically in this paper is how to successfully identify a caller in a cluttered environment with large uncertainties involved in the sensed audio-visual cues. The proposed approach is based on a proposition that the dependability of perceptual recognition may come unlikely from "the effort to make individual sensing perfect", but likely from "the effort to self-generate perceptual behaviors of integrating individual sensing that lead to mission accomplishment, no matter how imperfect and uncertain individual sensing may be". We implement the above proposition in terms of a novel robotic architecture, referred to here as "cognitive robotic engine (CRE)." CRE implemented for the case of a robot identifying a caller in a crowded and noisy environment, including its experimental results, are shown