A study on the transparency and acceptance of the predictive behaviors of vacuum robot
With the development of Artificial intelligence and Machine learning capabilities, the connected objects are extended with predictive capabilities, and the character of things is changed to “things that predict” (Smit, 2021). If a connected device is able to embrace a predictive system that not only profiles for scripted behavior but could also use the knowledge co-created by all the other similar devices and their users that encounter similar situations, the predictions can be generated based on that. In this case, a new type of interplay between humans and things called “predictive relation” is created.
However, challenges such as the transparency and users’ acceptance of predictive behavior of the everyday connected product still require us to figure out before the future takes place. It is urged to understand the now and the future, and this leads to the question: ‘how to design transparent and acceptable predictive relations for the things that predict?’
Therefore, to investigate the question, the project will explore the predictive relations and identify the design qualities for predictive relations between humans and things by taking the XiaoMi’s Vacuum robot as the starting point of the case study. The purpose of this project is to explore the predictive relations and study the transparency and the acceptance of the predictive behavior.
In answering the initial research question, this project proposed 2 propositions for transparency and acceptance respectively by combining the insights generated from the case study of XiaoMi’s vacuum robot and the creative session of envisioning the working of predicting vacuum robots.
The 2 propositions are evaluated through the method of ‘Wizard-of-oz’ and proved valid by combining the results of quantitative and qualitative research. As one of the results of the evaluation, this project also proposes the idea of “Designer as the facilitator of the human-robot collaboration”. The designers can be the ones who help to bring in the background knowledge and the patterns of the predictive relation and indicate the ways for humans and robots to co-perform reliable and meaningful daily practice in their partnership.
Eventually, this project takes designing a guidebook as one of the ways to clarify the idea and integrates the main results of the project into the guidebook to facilitate the collaboration between humans and predicting robots.
| University | Delft University of Technology/ Industrial Design Engineering |
| Type of project | Master Graduation |
| Maker(s) | Peicheng Guo |