Lessons Learned About Designing and Conducting Studies From HRI Experts

The field of human-robot interaction (HRI) research is multidisciplinary and requires researchers to understand diverse fields including computer science, engineering, informatics, philosophy, psychology, and more disciplines. However, it is hard to be an expert in everything. To help HRI researchers develop methodological skills, especially in areas that are relatively new to them, we conducted a virtual workshop, Workshop Your Study Design (WYSD), at the 2021 International Conference on HRI. In this workshop, we grouped participants with mentors, who are experts in areas like real-world studies, empirical lab studies, questionnaire design, interview, participatory design, and statistics. During and after the workshop, participants discussed their proposed study methods, obtained feedback, and improved their work accordingly. In this paper, we present 1) Workshop attendees’ feedback about the workshop and 2) Lessons that the participants learned during their discussions with mentors. Participants’ responses about the workshop were positive, and future scholars who wish to run such a workshop can consider implementing their suggestions. The main contribution of this paper is the lessons learned section, where the workshop participants contributed to forming this section based on what participants discovered during the workshop. We organize lessons learned into themes of 1) Improving study design for HRI, 2) How to work with participants - especially children -, 3) Making the most of the study and robot’s limitations, and 4) How to collaborate well across fields as they were the areas of the papers submitted to the workshop. These themes include practical tips and guidelines to assist researchers to learn about fields of HRI research with which they have limited experience. We include specific examples, and researchers can adapt the tips and guidelines to their own areas to avoid some common mistakes and pitfalls in their research

Lessons Learned About Designing and Conducting Studies From HRI Experts.

The field of human-robot interaction (HRI) research is multidisciplinary and requires researchers to understand diverse fields including computer science, engineering, informatics, philosophy, psychology, and more disciplines. However, it is hard to be an expert in everything. To help HRI researchers develop methodological skills, especially in areas that are relatively new to them, we conducted a virtual workshop, Workshop Your Study Design (WYSD), at the 2021 International Conference on HRI. In this workshop, we grouped participants with mentors, who are experts in areas like real-world studies, empirical lab studies, questionnaire design, interview, participatory design, and statistics. During and after the workshop, participants discussed their proposed study methods, obtained feedback, and improved their work accordingly. In this paper, we present 1) Workshop attendees' feedback about the workshop and 2) Lessons that the participants learned during their discussions with mentors. Participants' responses about the workshop were positive, and future scholars who wish to run such a workshop can consider implementing their suggestions. The main contribution of this paper is the lessons learned section, where the workshop participants contributed to forming this section based on what participants discovered during the workshop. We organize lessons learned into themes of 1) Improving study design for HRI, 2) How to work with participants - especially children -, 3) Making the most of the study and robot's limitations, and 4) How to collaborate well across fields as they were the areas of the papers submitted to the workshop. These themes include practical tips and guidelines to assist researchers to learn about fields of HRI research with which they have limited experience. We include specific examples, and researchers can adapt the tips and guidelines to their own areas to avoid some common mistakes and pitfalls in their research

Programmation de cobots : de l'apprentissage de trajectoires à leur acceptabilité

Robots, new machines in our daily lives, diversify. Recent progress has made the rising of cobots possible. Cobots are robots which collaborate with human beings. Contrary to traditional robots, this new type of robot requires the expertise of an operator to run. The Learning from Demonstration creates an original way of programming. The operator can manipulate the robot’s arm in order to teach it the movement to realize. The present thesis proposes an improvement of this learning through these three axes: the data processing, the learning, and the acceptability. Before being used by the learning, data is retrieved during the kinesthetic demonstration, then temporally aligned, and filtered to improve its quality. A novel learning algorithm with weighted data is proposed with generic software architecture allowing it to run on multiple robotics platforms. Finally, the acceptability of the Programming by Demonstration is evaluated with an experiment whose participants are potentially future users of cobots. The impact of the anthropomorphism is also considered. The different outcomes permit to consider the introduction of cobots in the industry of the future: from the data acquisition to the learning while evaluating the acceptability as well as the understanding of this type of programming by users.Les robots, nouvelles machines présentes dans nos vies quotidiennes, se diversifient. De récents progrès ont permis l’émergence des cobots, robots qui collaborent avec les êtres humains. Ce nouveau type de robots, contrairement aux robots traditionnels, nécessite l’expertise des opérateurs pour fonctionner. L’apprentissage par démonstration offre alors une méthode inédite de programmation. L’opérateur peut manipuler le bras du robot pour lui apprendre le mouvement à réaliser. Cette thèse propose de développer cet apprentissage par démonstration à travers plusieurs prismes de lecture : les données, l’apprentissage et l’acceptabilité. Avant d’être utilisées par l’apprentissage, les données sont obtenues à l’aide de démonstrations kinesthésiques puis alignées temporellement et filtrées afin d’en améliorer la qualité. Un nouvel algorithme d’apprentissage avec des données pondérées est proposé avec une architecture logicielle générique, fonctionnant sur de nombreuses plateformes robotiques. Finalement, l’acceptabilité de la programmation par démonstration est vérifiée par une expérience avec des participants, potentiels futurs utilisateurs des cobots. L’impact de l’anthropomorphisme est également pris en considération. Les différents résultats obtenus permettent d’envisager l’implantation des cobots dans l’industrie du futur : de l’acquisition des données en passant par l’apprentissage tout en vérifiant l’acceptabilité et la bonne compréhension de la programmation par les utilisateurs

Programmation de cobots : de l'apprentissage de trajectoires à leur acceptabilité

Robots, new machines in our daily lives, diversify. Recent progress has made the rising of cobots possible. Cobots are robots which collaborate with human beings. Contrary to traditional robots, this new type of robot requires the expertise of an operator to run. The Learning from Demonstration creates an original way of programming. The operator can manipulate the robot’s arm in order to teach it the movement to realize. The present thesis proposes an improvement of this learning through these three axes: the data processing, the learning, and the acceptability. Before being used by the learning, data is retrieved during the kinesthetic demonstration, then temporally aligned, and filtered to improve its quality. A novel learning algorithm with weighted data is proposed with generic software architecture allowing it to run on multiple robotics platforms. Finally, the acceptability of the Programming by Demonstration is evaluated with an experiment whose participants are potentially future users of cobots. The impact of the anthropomorphism is also considered. The different outcomes permit to consider the introduction of cobots in the industry of the future: from the data acquisition to the learning while evaluating the acceptability as well as the understanding of this type of programming by users.Les robots, nouvelles machines présentes dans nos vies quotidiennes, se diversifient. De récents progrès ont permis l’émergence des cobots, robots qui collaborent avec les êtres humains. Ce nouveau type de robots, contrairement aux robots traditionnels, nécessite l’expertise des opérateurs pour fonctionner. L’apprentissage par démonstration offre alors une méthode inédite de programmation. L’opérateur peut manipuler le bras du robot pour lui apprendre le mouvement à réaliser. Cette thèse propose de développer cet apprentissage par démonstration à travers plusieurs prismes de lecture : les données, l’apprentissage et l’acceptabilité. Avant d’être utilisées par l’apprentissage, les données sont obtenues à l’aide de démonstrations kinesthésiques puis alignées temporellement et filtrées afin d’en améliorer la qualité. Un nouvel algorithme d’apprentissage avec des données pondérées est proposé avec une architecture logicielle générique, fonctionnant sur de nombreuses plateformes robotiques. Finalement, l’acceptabilité de la programmation par démonstration est vérifiée par une expérience avec des participants, potentiels futurs utilisateurs des cobots. L’impact de l’anthropomorphisme est également pris en considération. Les différents résultats obtenus permettent d’envisager l’implantation des cobots dans l’industrie du futur : de l’acquisition des données en passant par l’apprentissage tout en vérifiant l’acceptabilité et la bonne compréhension de la programmation par les utilisateurs

Gaussian Mixture Model with Weighted Data for Learning by Demonstration

International audienceCobots are robots specialized in collaborating with a human to do a task. These cobots needs to be easily re-programmed in order to adapt to a new task. Learning by Demonstration enables a non-expert user to program a cobot by demonstrating how to realize the task. Once the learning is done, the user can only improve the learning by adding new demonstrations or deleting existing ones. In this article, the proposed model gives the possibility to the user to impact the learning by choosing which parts of the demonstration has more importance. This model uses an extended version of Gaussian Mixture Model (GMM) with weighted data coupled with Gaussian Mixture Regression (GMR). This architecture was tested with two different tasks and with two robots. Results indicate better generated trajectory with the proposed approach

Temporal Alignment and Demonstration Selection as Pre-Processing Phase for Learning by Demonstration

International audienceRobots can benefit from users’ demonstrations to learnmotions. To be efficient, a pre-processing phase needsto be performed on data recorded from demonstrations.This paper presents pre-processing methods developedfor Learning By Demonstration (LbD). Thepre-processing phase consists in methods composedof alignment algorithms and algorithms that select thegood demonstrations. In this paper we propose sixmethods and compare them to select the best one

RoboCup@Home Education 2020 Best Performance: RoboBreizh, a modular approach

Every year, the Robocup@Home competition challenges teams and robots' abilities. In 2020, the RoboCup@Home Education challenge was organized online, altering the usual competition rules. In this paper, we present the latest developments that lead the RoboBreizh team to win the contest. These developments include several modules linked to each other allowing the Pepper robot to understand, act and adapt itself to a local environment. Up-to-date available technologies have been used for navigation and dialogue. First contribution includes combining object detection and pose estimation techniques to detect user's intention. Second contribution involves using Learning by Demonstrations to easily learn new movements that improve the Pepper robot's skills. This proposal won the best performance award of the 2020 RoboCup@Home Education challenge