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.
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.
Cobots 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.
Robots 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.
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