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The use of collaborative robots (cobots) in industrial and academic settings facilitates physical and cognitive interaction with operators. This framework is a challenge to determine how measures on concepts, such as usability, can be adapted to these new environments. Usability is a quality attribute prevalent in the field of human-computer interaction concerning the context of use and the measure of effectiveness, efficiency, and satisfaction of products and systems. In this work, the importance of the role of benchmarking usability with collaborative robots is discussed. The introduced approach is part of a general methodology for studying people and robots' performance in collaboration. It is being designed and developed on a concrete experience into a human-robot collaborative workspace. Outcomes from the study include a list of steps, resources, recommendations, and some customized questionnaires to obtain cobot-oriented usability analysis and case study results. [ABSTRACT FROM AUTHOR]

In the industrial domain, one important research activity for cognitive robotics is the development of assistant robots. In this work, we show how the use of a cognitive assistant robot can contribute to (i) improving task effectiveness and productivity, (ii) providing autonomy for the human supervisor to make decisions, providing or improving human operators' skills, and (iii) giving feedback to the human operator in the loop. Our approach is evaluated on variability reduction in a manual assembly system. The overall study and analysis are performed on a model of the assembly system obtained using the Functional Resonance Analysis Method (FRAM) and tested in a robotic simulated scenario. Results show that a cognitive assistant robot is a useful partner in the role of improving the task effectiveness of human operators and supervisors. [ABSTRACT FROM AUTHOR]

The field of human-robot interaction (HRI) is a broad community encompassing robotics, artificial intelligence (AI), human-computer interaction (HCI), psychology and social science. HRI in social robotics explores important issues in designing a robot system that works with people in daily life environments, capable of interacting with, modeling, and learning from humans. Robotic systems should improve their capabilities to not only understand humans but also to convey their intention within their actions. The present work demonstrates this behaviour is achievable through a field study conducted at a science museum. This article introduces a computer vision algorithm which is able to detect and track a leader within a group of people - the science communicator, for this particular case - and distinguish between group members and non-group members as well, all by means of a cognitive and logical behaviour analysis of their interactions on scene. The leader's direction is also computed as an attention reference for this approach. The computer vision system is supervising people within a group following a guide to prevent accidents and missing persons. This work represents one of a wide range of possible applications and future scenarios where group interactions are a key aspect for robots to understand and effectively participate in social environments. [ABSTRACT FROM AUTHOR]

Natural and intuitive human interaction with robotic systems is a key point to develop robots assisting people in an easy and effective way. In this paper, a Human Robot Interaction (HRI) system able to recognize gestures usually employed in human non-verbal communication is introduced, and an in-depth study of its usability is performed. The system deals with dynamic gestures such as waving or nodding which are recognized using a Dynamic Time Warping approach based on gesture specific features computed from depth maps. A static gesture consisting in pointing at an object is also recognized. The pointed location is then estimated in order to detect candidate objects the user may refer to. When the pointed object is unclear for the robot, a disambiguation procedure by means of either a verbal or gestural dialogue is performed. This skill would lead to the robot picking an object in behalf of the user, which could present difficulties to do it by itself. The overall system — which is composed by a NAO and Wifibot robots, a Kinect TM v2 sensor and two laptops — is firstly evaluated in a structured lab setup. Then, a broad set of user tests has been completed, which allows to assess correct performance in terms of recognition rates, easiness of use and response times. [ABSTRACT FROM AUTHOR]

Research studies in Human Robot Interaction (HRI) with social robots usually gather observational data in order to explore the dynamics of short and long-term interactions. The most common approach for the analysis of observational data is the proposal of a small number of behavioural units which frequency and/or duration is captured. As a consequence, comparing results between studies is difficult. The present manuscript proposes a procedure to assess the complete human-robot interactive activity. Experiences with two different robots were analysed using the novel instrumentation, leading tofurther considerations. Finally, general guidelines extracted from experimentation are proposed to assess the interaction quality between social robots and users. Further studies can be benefited from the proposed instruments, which are expected to be validated in different HRI contexts (e.g., school, hospitals, home) with different users (e.g., children, elderly, hospitalised people). [ABSTRACT FROM AUTHOR]

In human–robot collaborative assembly tasks, it is necessary to properly balance skills to maximize productivity. Human operators can contribute with their abilities in dexterous manipulation, reasoning and problem solving, but a bounded workload (cognitive, physical, and timing) should be assigned for the task. Collaborative robots can provide accurate, quick and precise physical work skills, but they have constrained cognitive interaction capacity and low dexterous ability. In this work, an experimental setup is introduced in the form of a laboratory case study in which the task performance of the human–robot team and the mental workload of the humans are analyzed for an assembly task. We demonstrate that an operator working on a main high-demanding cognitive task can also comply with a secondary task (assembly) mainly developed for a robot asking for some cognitive and dexterous human capacities producing a very low impact on the primary task. In this form, skills are well balanced, and the operator is satisfied with the working conditions. [ABSTRACT FROM AUTHOR]