Exploiting the robot kinematic redundancy for emotion conveyance to humans as a lower priority task

Current approaches do not allow robots to execute a task and simultaneously convey emotions to users using their body motions. This paper explores the capabilities of the Jacobian null space of a humanoid robot to convey emotions. A task priority formulation has been implemented in a Pepper robot which allows the specification of a primary task (waving gesture, transportation of an object, etc.) and exploits the kinematic redundancy of the robot to convey emotions to humans as a lower priority task. The emotions, defined by Mehrabian as points in the pleasure–arousal–dominance space, generate intermediate motion features (jerkiness, activity and gaze) that carry the emotional information. A map from this features to the joints of the robot is presented. A user study has been conducted in which emotional motions have been shown to 30 participants. The results show that happiness and sadness are very well conveyed to the user, calm is moderately well conveyed, and fear is not well conveyed. An analysis on the dependencies between the motion features and the emotions perceived by the participants shows that activity correlates positively with arousal, jerkiness is not perceived by the user, and gaze conveys dominance when activity is low. The results indicate a strong influence of the most energetic motions of the emotional task and point out new directions for further research. Overall, the results show that the null space approach can be regarded as a promising mean to convey emotions as a lower priority task.Postprint (author's final draft

Advances in Human-Robot Interaction

Rapid advances in the field of robotics have made it possible to use robots not just in industrial automation but also in entertainment, rehabilitation, and home service. Since robots will likely affect many aspects of human existence, fundamental questions of human-robot interaction must be formulated and, if at all possible, resolved. Some of these questions are addressed in this collection of papers by leading HRI researchers

Exploring Human Teachers' Interpretations of Trainee Robots' Nonverbal Behaviour and Errors

In the near future, socially intelligent robots that can learn new tasks from humans may become widely available and gain an opportunity to help people more and more. In order to successfully play a role, not only should intelligent robots be able to interact effectively with humans while they are being taught, but also humans should have the assurance to trust these robots after teaching them how to perform tasks. When human students learn, they usually provide nonverbal cues to display their understanding of and interest in the material. For example, they sometimes nod, make eye contact or show meaningful facial expressions. Likewise, a humanoid robot's nonverbal social cues may enhance the learning process, in case the provided cues are legible for human teachers. To inform designing such nonverbal interaction techniques for intelligent robots, our first work investigates humans' interpretations of nonverbal cues provided by a trainee robot. Through an online experiment (with 167 participants), we examine how different gaze patterns and arm movements with various speeds and different kinds of pauses, displayed by a student robot when practising a physical task, impact teachers' understandings of the robot’s attributes. We show that a robot can appear differently in terms of its confidence, proficiency, eagerness to learn, etc., by systematically adjusting those nonverbal factors. Human students sometimes make mistakes while practising a task, but teachers may be forgiving about them. Intelligent robots are machines, and therefore, they may behave erroneously in certain situations. Our second study examines if human teachers for a robot overlook its small mistakes made when practising a recently taught task, in case the robot has already shown significant improvements. By means of an online rating experiment (with 173 participants), we first determine how severe a robot’s errors in a household task (i.e., preparing food) are perceived. We then use that information to design and conduct another experiment (with 139 participants) in which participants are given the experience of teaching trainee robots. According to our results, perceptions of teachers improve as the robots get better in performing the task. We also show that while bigger errors have a greater negative impact on human teachers' trust compared with the smaller ones, even a small error can significantly destroy trust in a trainee robot. This effect is also correlated with the personality traits of participants. The present work contributes by extending HRI knowledge concerning human teachers’ understandings of robots, in a specific teaching scenario when teachers are observing behaviours that have the primary goal of accomplishing a physical task

Advanced Automation for Space Missions

The feasibility of using machine intelligence, including automation and robotics, in future space missions was studied

Exploiting the robot kinematic redundancy for emotion conveyance to humans as a lower priority task

Current approaches do not allow robots to execute a task and simultaneously convey emotions to users using their body motions. This paper explores the capabilities of the Jacobian null space of a humanoid robot to convey emotions. A task priority formulation has been implemented in a Pepper robot which allows the specification of a primary task (waving gesture, transportation of an object, etc.) and exploits the kinematic redundancy of the robot to convey emotions to humans as a lower priority task. The emotions, defined by Mehrabian as points in the pleasure–arousal–dominance space, generate intermediate motion features (jerkiness, activity and gaze) that carry the emotional information. A map from this features to the joints of the robot is presented. A user study has been conducted in which emotional motions have been shown to 30 participants. The results show that happiness and sadness are very well conveyed to the user, calm is moderately well conveyed, and fear is not well conveyed. An analysis on the dependencies between the motion features and the emotions perceived by the participants shows that activity correlates positively with arousal, jerkiness is not perceived by the user, and gaze conveys dominance when activity is low. The results indicate a strong influence of the most energetic motions of the emotional task and point out new directions for further research. Overall, the results show that the null space approach can be regarded as a promising mean to convey emotions as a lower priority task

Producció científica de l'ETSEIB a Futur. Articles publicats per investigadors de l'ETSEIB l'any 2017

Postprint (author's final draft

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

RFID Technology in Intelligent Tracking Systems in Construction Waste Logistics Using Optimisation Techniques

Construction waste disposal is an urgent issue for protecting our environment. This paper proposes a waste management system and illustrates the work process using plasterboard waste as an example, which creates a hazardous gas when land filled with household waste, and for which the recycling rate is less than 10% in the UK. The proposed system integrates RFID technology, Rule-Based Reasoning, Ant Colony optimization and knowledge technology for auditing and tracking plasterboard waste, guiding the operation staff, arranging vehicles, schedule planning, and also provides evidence to verify its disposal. It h relies on RFID equipment for collecting logistical data and uses digital imaging equipment to give further evidence; the reasoning core in the third layer is responsible for generating schedules and route plans and guidance, and the last layer delivers the result to inform users. The paper firstly introduces the current plasterboard disposal situation and addresses the logistical problem that is now the main barrier to a higher recycling rate, followed by discussion of the proposed system in terms of both system level structure and process structure. And finally, an example scenario will be given to illustrate the system’s utilization

How to improve learning from video, using an eye tracker

The initial trigger of this research about learning from video was the availability of log files from users of video material. Video modality is seen as attractive as it is associated with the relaxed mood of watching TV. The experiments in this research have the goal to gain more insight in viewing patterns of students when viewing video. Students received an awareness instruction about the use of possible alternative viewing behaviors to see whether this would enhance their learning effects. We found that: - the learning effects of students with a narrow viewing repertoire were less than the learning effects of students with a broad viewing repertoire or strategic viewers. - students with some basic knowledge of the topics covered in the videos benefited most from the use of possible alternative viewing behaviors and students with low prior knowledge benefited the least. - the knowledge gain of students with low prior knowledge disappeared after a few weeks; knowledge construction seems worse when doing two things at the same time. - media players could offer more options to help students with their search for the content they want to view again. - there was no correlation between pervasive personality traits and viewing behavior of students. The right use of video in higher education will lead to students and teachers that are more aware of their learning and teaching behavior, to better videos, to enhanced media players, and, finally, to higher learning effects that let users improve their learning from video