Collaborative Artificial Intelligence Development for Social Robots

The main aim of this doctoral thesis was to investigate on how to involve a community for collaborative artificial intelligence (AI) development of a social robot. The work was initiated by the author’s personal interest in developing the Sony AIBO robots that have been unavailable on the retail markets, however, user communities with special interests in these robots remained on the internet. At first, to attract people’s attention, the author developed three specific features for the robot. These consisted of teaching the robot 1) sound event recognition in order to react to environmental audio stimuli, 2) a method to detect the underlying surface under the robot, and 3) of how to recognize its own body states. As this AI development proved to be very challenging, the author decided to start a community project for artificial intelligence development. Community involvement has a long history in open-source software projects and some robotics companies tried to benefit from their userbase in product development. An active online community of Sony AIBO owners was approached to investigate factors to engage its members in the creative processes. For this purpose, 78 Sony AIBO owners were recruited online to fill a questionnaire and their data were analyzed with respect to age, gender, culture, length of ownership, user contribution, and model preference. The results revealed the motives to own these robots for many years and how these heavy users perceived their social robots after a long period in the robot acceptance phase. For example, female participants tended to have more emotional relation to their robots than male who had more technically oriented long-term engagement motivation. The user expectations were also explored by analyzing the answers to this questionnaire to discover the key needs of this user group. The results revealed that the most-wanted skills were the interaction with humans and the autonomous operation. The integration with the AI agents and Internet services was important, but the long-term memory and learning capabilities were not so relevant for the participants. The diverse preferences for robot skills led to creating a prioritized recommendation list to complement the design guidelines for social robots in the literature. In sum, the findings of this thesis showed that developing AI features for an outdated robot is possible but takes a lot of time and shared community efforts. To involve a specific community, one needs first to build up trust by working with and for the community. Also, the trust for the long-term endurance of the development project was found as a precondition for the community commitment. The discoveries of this thesis can be applied to similar types of collaborative AI developments in the future. There are significant contributions in this dissertation to robotics. First, the long-term robot usage was not studied on a years-long scale before and the most extended human-robot interactions analyzed test subjects for only a few months. A questionnaire investigated the robot owners with 1-10+ years-long ownership in this work and their attitude towards robot acceptance. The survey results helped to understand the viable strategies to engage users for a long time. Second, innovative ways were explored to involve online communities in robotics development. The past approaches introduced the community ideas and opinions into product design and innovation iterations. The community in this dissertation tested the developed AI engine, provided inputs for further development directions, created content for the actual AI and gave their feedback about product quality. These contributions advance the social robotics field