Showing papers by "Amélie Cordier published in 2019"

The RoPE scale: a measure of how empathic a robot is perceived

Abstract: To be accepted in our everyday life and to be valuable interaction partners, robots should be able to display emotional and empathic behaviors. That is why there has been a great focus on developing empathy in robots in recent years. However, there is no consensus on how to measure how much a robot is considered to be empathic. In this context, we decided to construct a questionnaire which specifically measures the perception of a robot's empathy in human-robot interaction (HRI). Therefore we conducted pretests to generate items. These were validated by experts and will be further validated in an experimental setting.

Social Robots in Collaborative Learning: Consequences of the Design on Students' Perception

Abstract: The interest in using social robots in education is growing as it appears that they could add a social dimension that enhances learning. However, there is little use of robotics in collaborative learning contexts. This shows a lack of knowledge about students’ perception of social robots and their use for education purposes. This paper aims to fill this gap by analyzing, with experimental methods: (1) the influence of specific ways of interaction (facial expressions, voice and text) on the students’ perception of the robot and, (2) students’ acceptability criteria for using robots in a classroom. The target objective is to help the design of future learning situations. The study shows that the ways used to interact produce significant differences in the perception of the animation, the likeability, the attractiveness, the safety and the usability of the robot. The study also shows that major improvements must be made on the design of the hedonic characteristics of the interactions, especially identification and stimulation, to favor the student’s acceptance of this kind of learning

Social Robot as an Awareness Tool to Help Regulate Collaboration

Abstract: In collaborative learning, group awareness is a central issue. Being aware of the group's perceptions allows an adequate regulation of the activity. Our research explores whether a social robot can provide the necessary awareness. This study evaluates the usability and acceptability of a social robot used in such a role. The robot can express emotions and move according to territoriality principles, leading to a novel communication strategy. We designed and evaluated a learning situation where a Cozmo robot is included in a project meeting. As an awareness tool, it moves and expresses specific emotions that represent individual and group feelings to regulate learner communication behaviors.

Interactive Interpretation of Serial Episodes: experiments in musical analysis.

Abstract: The context of this work is the study of sequential data that can be represented with sequences of timestamped events. The aim is to explore these sequences with sequence mining to discover serial episodes which are frequent event subsequences that occur frequently in data (Mannila et al., 1997). The domain of melodic analysis is studied in this work : the aim is to highlight the structure of a musical piece by discovering its main melodic patterns. The episodes produced by the miner are examined by a user generally an expert of the domain who have to identify relevant episodes and interpret them. Meanwhile in the interpretation step, the user has to face to a recurrent overabundance of mining's results which makes difficult the identification of interesting ones. There is a real need to adopt a rigorous approach to methodically manage this step and assist the user's work. For this, we propose a visual and interactive approach to assist the interpretation of serial episodes. An Interactive approach to the interpretation of serial episodes We propose to assist the interpretation task by managing combinatorial redundancy in order to focus on relevant episodes. The assistance combines iteratively ranking and filtering useless episodes to help focusing on relevant ones. It has been exemplified in the Transmute prototype, a web-based application enabling user's interaction with events sequences and serial episodes that are represented graphically on a timeline with customisable icons. The interpretation process consists in the main iterative steps : ranking, selection and filtering. The user can choose measures to rank episodes and then select among them to display their occurrences in the sequence. When a choice is made, a filtering process is triggered to clean up other episodes that can no longer be selected following the previous selections of the user. Finally, the user can interpret the episodes by attaching them annotations and record the model resulting from the interpretation into a knowledge base. The ranking of episodes is performed thanks to several objective interestingness measures which estimate the relative importance and compactness of the episodes in the sequence. The first measure is the event coverage indicator which is the number of distinct events of the occurrences of an episode. The second measure is the spreading indicator which is the number of events of the sequence in the time intervals of the episode occurrences. The noise indicator is the difference between these two previous indicators and corresponds to the number of events of the sequence in the time intervals of the episode occurrences. Temporal measures may also be used when event duration are known. The selection of an episode by the user triggers the filtering process which is based on the event coverage of the selected episode. The remaining episodes are examined and occurrences having at least an event in common with the event coverage are discarded. The support is consequently updated and episodes whose support becomes less than the given frequency threshold are discarded. This results in removing combinatorial redundancy around the chosen episode and leads to a gradual diminution of the remaining episodes, allowing to the user a better focus on other episodes.