Collaborative storytelling has long been a goal of social robotics, however, much of this research is limited in interactivity or assumes that story content is curated. In this paper, we present a working fully-automatic collaborative storytelling robot, which can collaborate with a person to create a unique, improvised story by using a large-scale neural language model to dynamically generate continuations to a story. Because effective storytelling requires engaging the emotions of participants, we explore several modalities of procedurally-generated expressivity: 1. an expressive text-to-speech voice with several delivery styles, 2. physical and verbal reactions performed by the robot, and 3. an external display used to show instructions and graphics during storytelling.

To understand the issues associated with improvised collaborative storytelling with a social robot, we conduct an online survey and elicitation study with a group of online observers of collaborative storytelling gameplay, comparing several expressivity strategies in terms of storytelling-related characteristics, expressivity characteristics, and personality traits as measured by RoSAS. This evaluation showed that expressivity strategies using both emotive voice and performed reactions were perceived to be more competent storytellers and more strongly associated with positive personality traits.

Robot-mediated Group Instruction for Children with ASD: A Pilot Study

Population around Europe is getting older; many older adults would like to live independently in their homes as long as possible. Personalized integrated care plan strategies envisage
the possibility of home care services for those people at risk of frailty. In this context, the robot could empower people to live independently. In the presented work, 20 frail older adults were asked to live with a telepresence robot (i.e., Double Robot), through
which the formal caregiver could remotely visit them. A total of 284
days of field-test trial was evaluated with a proposed framework
that explores several domains, including system reliability, maintainability,
system acceptance, user attitude, and user experiences. At the end of the experimentation, the system was used for 4241 mins accomplishing 151 telepresence services, and the results underline optimal system reliability and high user experience. Based on the collected feedback, the proposed telepresence robot can be exploited into integrated care services. The evaluation of a system should envisage a complex, multidisciplinary, and holistic approach
that may influence the success or failure of the robot’s purpose if not properly analyzed during the evaluation and design phase.

Qinghua Chen

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