Course Content
Orientation, introduction to the course
1. Human-Robot Interaction (HRI)
2. Research Methods in Human-Robot Interaction
3. Smart Cities & HRI
The demand for city living is already high, and it appears that this trend will continue. According to the United Nations World Cities Report, by 2050, more than 70% of the world's population will be living and working in cities — one of many reports predicting that cities will play an important role in our future (UN-Habitat, 2022). Thus, as cities are growing in size and scope, it is shaped into complex urban landscape where things, data, and people interact with each other. Everything and everyone has become so connected that Wifi too often fails to meet digital needs, online orders don't arrive fast enough, traffic jams still clog the roads and environmental pollution still weighs on cities. New technologies, technical intelligence, and robots can contribute to the direction of finding solutions to ever-increasing problems and assist the evolution of the growing urban space.
Human-Robot Interaction
About Lesson

Opportunities and Challenges

Until now, robots have been used in environments that are controlled or semi-controlled, where human interaction is limited and controls are in place to ensure human safety. In order for urban robotics to be more widely applied, it is necessary to conduct meaningful real-world trials that will test and improve the technology (While et al., 2021).

Urban researchers are showing a growing interest in the ways that robotics and automation are transforming urban environments. With the advancements in machine learning and artificial intelligence, the potential for robots to interact with humans and operate in dynamic settings has expanded significantly. This could allow robots to perform more complex tasks in diverse environments. However, the threats and issues that can occur from applying machine learning algorithms to data about our interests, intentions, and activities have recently come into stark focus. Big data decision support has been characterized as a sort of coercive control that people find difficult to refuse. Although only 4% of the world’s population resides in North America, machine learning frequently trains on datasets that overrepresent the cultural practices and racial characteristics of that region. Careless training of machine learning algorithms with biased data can result in a sexist or racist bias. It is well known that malevolent actors may easily manipulate the “bubbles” those careless social media users form to spread their ideologies. Regardless of how different actors may utilize the data, we may be concerned

about our right to privacy, our right to anonymity, the security of our identity, and the openness of the process and foundation of decision-making. It has been stated that at least in China, people do not perceive the various schemes that have emerged from state and commercial entities that show financial probity, trust, and “loyalty” as a breach of their privacy but rather as helpful ways to enhance dependable, honest interactions. It is obvious that we see the use of big data and machine learning in our lives as, at best, a two-edged sword. Similar issues have been raised in the writings of various authors, which is not surprising given the technological perspective on the Smart City (Studley & Little, 2021).


While, A. H., Marvin, S., & Kovacic, M. (2021). Urban robotic experimentation: San Francisco, Tokyo and Dubai. Urban Studies, 58(4), 769-786.

Studley, M. E., & Little, H. (2021). Robots in smart cities. In How Smart Is Your City? (pp. 75-88). Springer, Cham.