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“How to behave in the situation [when different actors perceive robotic artifacts differently], whether you prefer to do this constantly in individual situations or with an entire group, how to deal with the dynamics. These are all things that can be better explained by such studies [on learning processes regarding robots] at some point.” (Paluch and Müller, 2022, p. 23).
This quote is an interview excerpt from our study in which robotic pets were used in a nursing home. One of our research interests was the creation of mutual learning spaces for the residents, care attendants, and university researchers. The care attendant had pointed out that co-creative learning spaces were necessary for developing an imagination on how the usage of robotic pets could be meaningful in different care situations. In our view, such spaces for mutual learning and co-creation are necessary to foster the development of functionally better technologies and reflect on their use with all participants.
We need to co-create spaces for people to appropriate new technologies such as robots (Stevens and Pipek, 2018). For this, three questions are important: (1) How to include spatial aspects in design? (2) What could be learned in various spaces? (3) What role does the body play in human-robot interactions? Thus, we define three interrelated foci:
(1) The emphasis is on not only developing a specific technology, but also having a space in which to explore a technology. A robot also needs an environment in which it can function and be used (Lindemann and Matsuzaki, 2014). That includes spatial aspects that we would like to emphasize in our Participatory Design (PD) approach (Simonsen and Robertson, 2013).
(2) How can learning processes be supported through PD? It is not only about how to use technology. One also needs to learn in which situations it is appropriate to use technologies (Cerna et al., 2022).
(3) The human-robot interaction is characterized by the fact that both the human and the robot have a body. Robots are embodied technical artifacts. This distinguishes robotic systems from other digital technologies (Bartneck et al., 2020, p. 7).

Creation of learning spaces through PD
In PD, emphasis is placed on allowing users to participate in all stages of design. Design ideas are gained by focusing on usage practices, and novel technical possibilities can be explored. This can be achieved by discovering a more creative solution to a problem. With PD methods, it is possible to strengthen mutual learning in an iterative development. In addition to the socio-cultural structures or technical artifacts, this also requires an environment in which collaborative participation is possible. Thus, PD studies need to create suitable learning spaces (Bratteteig and Wagner, 2012; Suchman, 2011). Compared to user-centered design, we would like to use the term PD to emphasize that this is a co-research process in which the emphasis is on the inclusion of all perspectives (Duque et al., 2019).
In co-creative learning spaces, where groups of people from different contexts take part, the sense of technologies could be negotiated together. Negotiating in spaces have been crucial in participatory research methods and Scandinavian design for decades based on the democratic ideal that everyone should have the right to participate in decisions impacting one’s own life (Ehn, 1993; Nygaard, 1996). In co-creative learning spaces, this approach has a central importance, as freedom of expression is an essential aspect in successful participation and negotiation, and at the same time contributes to maximizing the learning effect and empowerment (Brown et al., 2014; Ogonowski et al., 2018). Co-creation is consequently always a learning journey (Akhilesh, 2017), which makes it an appropriate format to support learning effectiveness and autonomy (Kaptelinin and Bannon, 2012; Vassilakopoulou et al., 2018).

Learning and empowerment through interaction with robotic technologies
Learning to use technical artifacts is an important practice that remains with people throughout their lives and takes place in various spaces, environments, and situations, covering formal, non-formal, and informal learning (Aoki, 2020). In societal discourse, current debate is about how lifelong and lifewide learning can be designed and supported (Zhuang et al., 2017; Findsen and Formosa, 2011; Fischer, 2000), and in which social contexts learning processes must be placed to generate positive impacts such as empowerment and improved living standards (Carr et al., 2018). For example, scientific studies refer to how older persons can learn technical skills so that they can use different technical artifacts (e.g., Lee and Riek, 2018; Schreurs et al., 2017). There are also a variety of digital technologies that teach people in everyday life (e.g., Cerna et al., 2022; Fong et al., 2021; Johnson et al., 2012; Kuoppamäki et al., 2021; van den Berghe et al., 2019).
In this context, empowerment describes a social construct or interpersonal process between at least two people in which tools, resources, and environments are combined and made available in ways that increase the capabilities of the empowered persons and develop new skills (Hawks, 1992). This empowerment supports individuals‘ abilities to make purposeful decisions and thus transform them into desired outcomes. Thus, empowerment not only describes the goal itself, but is also considered a driver for development (Alsop, 2006). Accordingly, it is also necessary to evoke the feeling of empowerment in the participating individuals in advance so that they can efficiently and effectively participate in the negotiation processes of co-creative methods (Fraser et al., 2006).

Human-robot interaction and embodiment
The different aspects of embodiment have been explored in various fields of science and from diverse perspectives, for example in studies in communication and psychology, modern philosophy, HCI, (E)CSCW and sociology. We align our work with existing (E)CSCW and HCI literature on embodied interaction and action (Al-Sada et al., 2021; Ducheneaut et al., 2009; Schmidt, 2018; Yamazaki et al., 2012). Our understanding of embodiment builds on two key points:
“First, that cognition depends upon the kinds of experience that comes from having a body with various sensorimotor capacities, and second, that these individual sensorimotor capacities are themselves embedded in a more encompassing biological, psychological, and cultural context” (Varela et al., 1991, pp. 172-173).
In other words, it is the bodily engagement with the social and physical environment that constitutes the interaction between humans and robots (Dourish, 2001).
Using the term “embodied”, we aim to highlight that both humans and robots interact with the world through their bodies (and not only their minds or software) and this active experience of the world shapes how they perceive their surroundings. The embodied aspect is crucial as it mediates our various human-robot interactions (Klemmer et al., 2006; Lindemann and Matsuzaki, 2014).

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