Reinforcement learning (RL) offers a promising solution for controlling humanoid robots, particularly for bipedal locomotion, by learning adaptive and flexible control strategies. However, direct RL application is hindered by time-consuming trial-and-error processes, necessitating training in simulation before real-world transfer. This introduces a reality gap that degrades performance. Although various methods have been proposed for sim-to-real transfer, they have not been validated on a consistent hardware platform, making it difficult to determine which components are key to overcoming the reality gap. In contrast, we systematically evaluate techniques to enhance RL policy robustness during sim-to-real transfer by controlling variables and comparing them on a single robot to isolate and analyze the impact of each technique. These techniques include dynamics randomization, state history usage, noise/bias/delay modeling, state selection, perturbations, and network size. We quantitatively assess the reality gap by simulating diverse conditions and conducting experiments on real hardware. Our findings provide insights into bridging the reality gap, advancing robust RL-trained humanoid robots for real-world applications.

Autonomous mobile robots (AMRs) are revolutionizing industries by enhancing flexibility and efficiency, particularly in dynamic environments, such as automotive manufacturing. These environments pose challenges due to their constantly changing layouts, unpredictable obstacles, and varying conditions, which impact the performance of localization systems. This article presents a novel real-time localization scoring architecture to address these challenges by quantifying the confidence in a robot’s positioning system. The proposed localization score improves map reconciliation, manages sensor interference, adapts navigation strategies, and enhances traffic coordination. Extensive experimental studies, including real-world deployment in an operational automotive production factory, demonstrate the robustness, accuracy, and adaptability of the developed localization score algorithm. The results showcase its potential to significantly enhance the operational efficiency and reliability of AMRs in industrial settings.

This article explores the advent of an innovative autonomous robot designed to enhance hospital disinfection through targeted wiping and ultraviolet-C (UV-C) irradiation methods. The urgency for such advancements has been underscored by the COVID-19 pandemic, which revealed challenges, such as low compliance rates, physical fatigue, labor shortages, and heightened risk of pathogen exposure for disinfection workers. Traditional disinfection approaches, including UV-C irradiation mobile robots and hydrogen peroxide vapor methods, while effective, fall short in addressing obstacles like shaded areas and surface contaminants. To bridge this gap, we introduce a novel robot that combines physical wiping to remove contaminants with targeted UV-C irradiation for areas less amenable to wiping. Our development efforts have centered on optimizing disinfection efficacy and ensuring the robot’s reliability for practical applications in real-world hospitals. The performance and usability of this autonomous disinfection robot were thoroughly assessed at Pohang St. Mary’s Hospital, demonstrating its potential to transform hospital disinfection practices by complementing traditional disinfection tasks with advanced robotic assistance.