Sequential annotations for naturally-occurring HRI: first insights
This work addresses the challenge of enhancing human-robot interaction for users in public spaces like libraries, but it appears incremental as it builds on existing theoretical frameworks without claiming major breakthroughs.
The researchers tackled the problem of improving interactions with an embedded conversational agent, specifically a Pepper robot in a library setting, by developing a methodology based on Conversation Analytic sequential and multimodal analysis, resulting in the creation of a corpus of naturally-occurring interactions for community use.
We explain the methodology we developed for improving the interactions accomplished by an embedded conversational agent, drawing from Conversation Analytic sequential and multimodal analysis. The use case is a Pepper robot that is expected to inform and orient users in a library. In order to propose and learn better interactive schema, we are creating a corpus of naturally-occurring interactions that will be made available to the community. To do so, we propose an annotation practice based on some theoretical underpinnings about the use of language and multimodal resources in human-robot interaction. CCS CONCEPTS $\bullet$ Computing methodologies $\rightarrow$ Discourse, dialogue and pragmatics; $\bullet$ Human-centered computing $\rightarrow$ Text input; HCI theory, concepts and models; Field studies.