Does anyone want to talk to me? : Reflections on the use of assistance and companion robots in care homes

Held at the AISB'15 ConventionFinal Accepted Versio

Teaching a Humanoid: A User Study on Learning by Demonstration with HOAP-3

This article reports on the results of a user study investigating the satisfaction of naïve users conducting two learning by demonstration tasks with the HOAP-3 robot. The main goal of this study was to gain insights on how to ensure a successful as well as satisfactory experience for naïve users. Participants performed two tasks: They taught the robot to (1) push a box, and to (2) close a box. The user study was accompanied by three pre-structured questionnaires, addressing the users’ satisfaction with HOAP-3, the user’s affect toward the robot following from the interaction, and the user’s attitude towards robots. Furthermore, a retrospective think aloud was conducted to gain a better understanding of what influences the users’ satisfaction in learning by demonstration tasks. The results stress that learning by demonstration is a promising approach for naïve users to learn the interaction with a robot, as a high task completion and final satisfaction rate could be observed. Moreover, the short term interaction with HOAP-3 led to a positive affect higher than the normative average on half of the female users

“Arisco” un robot social con capacidad de interaccion, motivacion y aprendizaje

ResumenEn este artículo se describe la arquitectura del robot social “Arisco” con especial atención a su sistemas de interacción, motivación, planificación y aprendizaje. Arisco es una cabeza mecatrónica con capacidad de interacción y que incluye: gran expresividad mediante gesticulación, reconocimiento y síntesis de voz, seguimiento visual, extracción de información de internet, y sistema de aprendizaje y motivación

Combining goal inference and natural-language dialogue for human-robot joint action

We demonstrate how combining the reasoning components from two existing systems designed for human-robot joint action produces an integrated system with greater capabilities than either of the individual systems. One of the systems supports primarily non-verbal interaction and uses dynamic neural fields to infer the user’s goals and to suggest appropriate system responses; the other emphasises natural-language interaction and uses a dialogue manager to process user input and select appropriate system responses. Combining these two methods of reasoning results in a robot that is able to coordinate its actions with those of the user while employing a wide range of verbal and non-verbal communicative actions.(undefined

A framework integrating statistical and social cues to teach a humanoid robot new skills

Bringing robots as collaborative partners into homes presents various challenges to human-robot interaction. Robots will need to interact with untrained users in environments that are originally designed for humans. Compared to their industrial homologous form, humanoid robots can not be preprogrammed with an initial set of behaviours. They should adapt their skills to a huge range of possible tasks without needing to change the environments and tools to fit their needs. The rise of these humanoids implies an inherent social dimension to this technology, where the end-users should be able to teach new skills to these robots in an intuitive manner, relying only on their experience in teaching new skills to other human partners. Our research aims at designing a generic Robot Programming by Demonstration (RPD) framework based on a probabilistic representation of the task constraints, which allows to integrate information from cross-situational statistics and from various social cues such as joint attention or vocal intonation. This paper presents our ongoing research towards bringing user- friendly human-robot teaching systems that would speed up the skill transfer process