Expressing Robot Personality through Talking Body Language

Social robots must master the nuances of human communication as a mean to convey an effective message and generate trust. It is well-known that non-verbal cues are very important in human interactions, and therefore a social robot should produce a body language coherent with its discourse. In this work, we report on a system that endows a humanoid robot with the ability to adapt its body language according to the sentiment of its speech. A combination of talking beat gestures with emotional cues such as eye lightings, body posture of voice intonation and volume permits a rich variety of behaviors. The developed approach is not purely reactive, and it easily allows to assign a kind of personality to the robot. We present several videos with the robot in two different scenarios, and showing discrete and histrionic personalities.This work has been partially supported by the Basque Government (IT900-16 and Elkartek 2018/00114), the Spanish Ministry of Economy and Competitiveness (RTI 2018-093337-B-100, MINECO/FEDER, EU)

Personalizing Human-Robot Dialogue Interactions using Face and Name Recognition

Task-oriented dialogue systems are computer systems that aim to provide an interaction indistinguishable from ordinary human conversation with the goal of completing user- defined tasks. They are achieving this by analyzing the intents of users and choosing respective responses. Recent studies show that by personalizing the conversations with this systems one can positevely affect their perception and long-term acceptance. Personalised social robots have been widely applied in different fields to provide assistance. In this thesis we are working on development of a scientific conference assistant. The goal of this assistant is to provide the conference participants with conference information and inform about the activities for their spare time during conference. Moreover, to increase the engagement with the robot our team has worked on personalizing the human-robot interaction by means of face and name recognition. To achieve this personalisation, first the name recognition ability of available physical robot was improved, next by the concent of the participants their pictures were taken and used for memorization of returning users. As acquiring the consent for personal data storage is not an optimal solution, an alternative method for participants recognition using QR Codes on their badges was developed and compared to pre-trained model in terms of speed. Lastly, the personal details of each participant, as unviversity, country of origin, was acquired prior to conference or during the conversation and used in dialogues. The developed robot, called DAGFINN was displayed at two conferences happened this year in Stavanger, where the first time installment did not involve personalization feature. Hence, we conclude this thesis by discussing the influence of personalisation on dialogues with the robot and participants satisfaction with developed social robot