In this study, we argue that the development of semiotically adaptive cognition is indispensable for realizing remotely-operated service robots to enhance the quality of the new normal society. To enable a wide range of people to work from home in a pandemic like the current COVID-19 situation, the installation of remotely-operated service robots into the work environment is crucial. However, it is evident that remotely-operated robots must have partial autonomy. The capability of learning local semiotic knowledge for improving autonomous decision making and language understanding is crucial to reduce the workload of people working from home. To achieve this goal, we refer to three challenges: the learning of local semiotic knowledge from daily human activities, the acceleration of local knowledge learning with transfer learning and active exploration, and the augmentation of human-robot interactions.