Learning and Comfort in Human–Robot Interaction: A Review

Assimilation of socially assistive robots by older adults: an interplay of uses, constraints and outcomes

By supporting autonomy, aging in place, and wellbeing in later life, Socially Assistive Robots are expected to help humanity face the challenges posed by the rapid aging of the world's population. For the successful acceptance and assimilation of SARs by older adults, it is necessary to understand the factors affecting their Quality Evaluations Previous studies examining Human-Robot Interaction in later life indicated that three aspects shape older adults' overall QEs of robots: uses, constraints, and outcomes. However, studies were usually limited in duration, focused on acceptance rather than assimilation, and typically explored only one aspect of the interaction. In the present study, we examined uses, constraints, and outcomes simultaneously and over a long period. Nineteen community-dwelling older adults aged 75-97 were given a SAR for physical training for 6 weeks. Their experiences were documented via in-depth interviews conducted before and after the study period, short weekly telephone surveys, and reports produced by the robots. Analysis revealed two distinct groups: (A) The 'Fans' - participants who enjoyed using the SAR, attributed added value to it, and experienced a successful assimilation process; and (B) The 'Skeptics' - participants who did not like it, negatively evaluated its use, and experienced a disappointing assimilation process. Despite the vast differences between the groups, both reported more positive evaluations of SARs at the end of the study than before it began. Overall, the results indicated that the process of SARs' assimilation is not homogeneous and provided a profound understanding of the factors shaping older adults' QE of SARs following actual use. Additionally, the findings demonstrated the theoretical and practical usefulness of a holistic approach in researching older SARs users.

Role of Reference Frames for a Safe Human-Robot Interaction

Safety plays a key role in human-robot interactions in collaborative robot (cobot) applications. This paper provides a general procedure to guarantee safe workstations allowing human operations, robot contributions, the dynamical environment, and time-variant objects in a set of collaborative robotic tasks. The proposed methodology focuses on the contribution and the mapping of reference frames. Multiple reference frame representation agents are defined at the same time by considering egocentric, allocentric, and route-centric perspectives. The agents are processed to provide a minimal and effective assessment of the ongoing human-robot interactions. The proposed formulation is based on the generalization and proper synthesis of multiple cooperating reference frame agents at the same time. Accordingly, it is possible to achieve a real-time assessment of the safety-related implications through the implementation and fast calculation of proper safety-related quantitative indices. This allows us to define and promptly regulate the controlling parameters of the involved cobot without velocity limitations that are recognized as the main disadvantage. A set of experiments has been realized and investigated to demonstrate the feasibility and effectiveness of the research by using a seven-DOF anthropomorphic arm in combination with a psychometric test. The acquired results agree with the current literature in terms of the kinematic, position, and velocity aspects; use measurement methods based on tests provided to the operator; and introduce novel features of work cell arranging, including the use of virtual instrumentation. Finally, the associated analytical-topological treatments have enabled the development of a safe and comfortable measure to the human-robot relation with satisfactory experimental results compared to previous research. Nevertheless, the robot posture, human perception, and learning technologies would have to apply research from multidisciplinary fields such as psychology, gesture, communication, and social sciences in order to be prepared for positioning in real-world applications that offer new challenges for cobot applications.

Learning from Demonstrations in Human–Robot Collaborative Scenarios: A Survey

Human–Robot Collaboration (HRC) is an interdisciplinary research area that has gained attention within the smart manufacturing context. To address changes within manufacturing processes, HRC seeks to combine the impressive physical capabilities of robots with the cognitive abilities of humans to design tasks with high efficiency, repeatability, and adaptability. During the implementation of an HRC cell, a key activity is the robot programming that takes into account not only the robot restrictions and the working space, but also human interactions. One of the most promising techniques is the so-called Learning from Demonstration (LfD), this approach is based on a collection of learning algorithms, inspired by how humans imitate behaviors to learn and acquire new skills. In this way, the programming task could be simplified and provided by the shop floor operator. The aim of this work is to present a survey of this programming technique, with emphasis on collaborative scenarios rather than just an isolated task. The literature was classified and analyzed based on: the main algorithms employed for Skill/Task learning, and the human level of participation during the whole LfD process. Our analysis shows that human intervention has been poorly explored, and its implications have not been carefully considered. Among the different methods of data acquisition, the prevalent method is physical guidance. Regarding data modeling, techniques such as Dynamic Movement Primitives and Semantic Learning were the preferred methods for low-level and high-level task solving, respectively. This paper aims to provide guidance and insights for researchers looking for an introduction to LfD programming methods in collaborative robotics context and identify research opportunities.

A Review on Human Comfort Factors, Measurements, and Improvements in Human-Robot Collaboration

As the development of robotics technologies for collaborative robots (COBOTs), the applications of human-robot collaboration (HRC) have been growing in the past decade. Despite the tremendous efforts from both academia and industry, the overall usage and acceptance of COBOTs are still not so high as expected. One of the major affecting factors is the comfort of humans in HRC, which is usually less emphasized in COBOT development; however, it is critical to the user acceptance during HRC. Therefore, this paper gives a review of human comfort in HRC including the influential factors of human comfort, measurement of human comfort in terms of subjective and objective manners, and human comfort improvement approaches in the context of HRC. Discussions on each topic are also conducted based on the review and analysis.

A Trustworthy Robot Buddy for Primary School Children

Social robots hold potential for supporting children’s well-being in classrooms. However, it is unclear which robot features add to a trustworthy relationship between a child and a robot and whether social robots are just as able to reduce stress as traditional interventions, such as listening to classical music. We set up two experiments wherein children interacted with a robot in a real-life school environment. Our main results show that regardless of the robotic features tested (intonation, male/female voice, and humor) most children tend to trust a robot during their first interaction. Adding humor to the robots’ dialogue seems to have a negative impact on children’s trust, especially for girls and children without prior experience with robots. In comparing a classical music session with a social robot interaction, we found no significant differences. Both interventions were able to lower the stress levels of children, however, not significantly. Our results show the potential for robots to build trustworthy interactions with children and to lower children’s stress levels. Considering these results, we believe that social robots provide a new tool for children to make their feelings explicit, thereby enabling children to share negative experiences (such as bullying) which would otherwise stay unnoticed.

Can Robots Get Some Human Rights? A Cross-Disciplinary Discussion

An autonomous household robot passed a self-awareness test in 2015, proving that the cognitive capabilities of robots are heading towards those of humans. While this is a milestone in AI, it raises questions about legal implications. If robots are progressively developing cognition, it is important to discuss whether they are entitled to justice pursuant to conventional notions of human rights. This paper offers a comprehensive discussion of this complex question through cross-disciplinary scholarly sources from computer science, ethics, and law. The computer science perspective dissects hardware and software of robots to unveil whether human behavior can be efficiently replicated. The ethics perspective utilizes insights from robot ethics scholars to help decide whether robots can act morally enough to be endowed with human rights. The legal perspective provides an in-depth discussion of human rights with an emphasis on eligibility. The article concludes with recommendations including open research issues.

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics

Repetitive industrial tasks can be easily performed by traditional robotic systems. However, many other works require cognitive knowledge that only humans can provide. Human-Robot Collaboration (HRC) emerges as an ideal concept of co-working between a human operator and a robot, representing one of the most significant subjects for human-life improvement.The ultimate goal is to achieve physical interaction, where handing over an object plays a crucial role for an effective task accomplishment. Considerable research work had been developed in this particular field in recent years, where several solutions were already proposed. Nonetheless, some particular issues regarding Human-Robot Collaboration still hold an open path to truly important research improvements. This paper provides a literature overview, defining the HRC concept, enumerating the distinct human-robot communication channels, and discussing the physical interaction that this collaboration entails. Moreover, future challenges for a natural and intuitive collaboration are exposed: the machine must behave like a human especially in the pre-grasping/grasping phases and the handover procedure should be fluent and bidirectional, for an articulated function development. These are the focus of the near future investigation aiming to shed light on the complex combination of predictive and reactive control mechanisms promoting coordination and understanding. Following recent progress in artificial intelligence, learning exploration stand as the key element to allow the generation of coordinated actions and their shaping by experience.

Cognitive Interaction Analysis in Human–Robot Collaboration Using an Assembly Task

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.

Facing with Collaborative Robots: The Subjective Experience in Senior and Younger Workers

In the past few years, collaborative robots (i.e., cobots) have been largely adopted within industrial manufacturing. Although robots can support companies and workers in carrying out complex activities and improving productivity, human factors related to cobot operators have not yet been thoroughly investigated. The present study aims to understand the subjective experience of younger and senior workers interacting with an industrial collaborative robot. Results show that workers' acceptance of cobots is high, regardless of age and control modality used. Interesting differences between seniors and younger adults emerged in the evaluations of user experience, usability, and perceived workload of participants and are detailed and commented in the last part of the work.

On Cognitive Assistant Robots for Reducing Variability in Industrial Human-Robot Activities

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.