Socially intelligent robots: dimensions of human-robot interaction

Social intelligence in robots has a quite recent history in artificial intelligence and robotics. However, it has become increasingly apparent that social and interactive skills are necessary requirements in many application areas and contexts where robots need to interact and collaborate with other robots or humans. Research on human-robot interaction (HRI) poses many challenges regarding the nature of interactivity and 'social behaviour' in robot and humans. The first part of this paper addresses dimensions of HRI, discussing requirements on social skills for robots and introducing the conceptual space of HRI studies. In order to illustrate these concepts, two examples of HRI research are presented. First, research is surveyed which investigates the development of a cognitive robot companion. The aim of this work is to develop social rules for robot behaviour (a 'robotiquette') that is comfortable and acceptable to humans. Second, robots are discussed as possible educational or therapeutic toys for children with autism. The concept of interactive emergence in human-child interactions is highlighted. Different types of play among children are discussed in the light of their potential investigation in human-robot experiments. The paper concludes by examining different paradigms regarding 'social relationships' of robots and people interacting with them.     

智能机器人在社区康复中的应用研究

我国正处于人口老龄化阶段,各种病患人群占据比例增加,如急性病、慢性病以及高致残病患群体术后康复需求加剧。住院康复增加了护理人员的劳动强度,患者康复耗时长、费用高等问题,因此社区居家康复成为患者的主要选择。针对目前社区康复方式存在缺乏有效的评估、监督及指导等问题,提出“智能机器人+社区康复”的解决方案。智能机器人具备数据采集、数据分析、控制决策、评估反馈、在线指导、协作康复、交互娱乐等诸多功能,能长时间反复为患者提供满意的康复服务,是社区居家康复的最佳选择。本文旨在提出现有社区康复面临的挑战,指出智能康复机器人发展的风向口,探讨智能康复机器人在推广运用过程中面临的各种挑战及解决方案,并对康复机器人在社区康复中的应用前景做出预判。     

Concept of Intelligent Mechanical Design for Autonomous Mobile Robots

The concept of Intelligent Mechanical Design (IMD) is presented to show how a mechanical structure can be designed to affect robot controllability, simplification and task performance. Exploring this concept produces landmarks in the territory of mechanical robot design in the form of seven design principles. The design principles, which we call the Mecha-Telligence Principles (MTP), provide guidance on how to design mechanics for autonomous mobile robots. These principles guide us to ask the right questions when investigating issues concerning self-controllable, reliable, feasible, and compatible mechanics for autonomous mobile robots. To show how MTP can be applied in the design process we propose a novel methodology, named as Mecha-Telligence Methodology (MTM). Mechanical design by the proposed methodology is based on preference classification of the robot specification described by interaction of the robot with its environment and the physical parameters of the robot mechatronics. After defining new terms, we investigate the feasibility of the proposed methodology to the mechanical design of an autonomous mobile sewer inspection robot. In this industrial project we show how a passive-active intelligent moving mechanism can be designed using the MTM and employed in the field.     

Focus: The Aging Brain: Social capital,health, and elderly driver status

Driving a car enables many people to engage in meaningful activities that, in turn, help develop and maintain personal social capital. Social capital, a combination of community participation and social cohesion, is important in maintaining well-being. This paper argues that social capital can provide a framework for investigating the general role of transportation and driving a car specifically to access activities that contribute to connectedness and well-being among older people. This paper proposes theoretically plausible and empirically testable hypotheses about the relationship between driver status, social capital, and well-being. A longitudinal study may provide a new way of understanding, and thus of addressing, the well-being challenges that occur when older people experience restrictions to, or loss of, their driver’s license.     

Emotion Attribution to a Non-Humanoid Robot in Different Social Situations

In the last few years there was an increasing interest in building companion robots that interact in a socially acceptable way with humans. In order to interact in a meaningful way a robot has to convey intentionality and emotions of some sort in order to increase believability. We suggest that human-robot interaction should be considered as a specific form of inter-specific interaction and that human–animal interaction can provide a useful biological model for designing social robots. Dogs can provide a promising biological model since during the domestication process dogs were able to adapt to the human environment and to participate in complex social interactions. In this observational study we propose to design emotionally expressive behaviour of robots using the behaviour of dogs as inspiration and to test these dog-inspired robots with humans in inter-specific context. In two experiments (wizard-of-oz scenarios) we examined humans'' ability to recognize two basic and a secondary emotion expressed by a robot. In Experiment 1 we provided our companion robot with two kinds of emotional behaviour (“happiness” and “fear”), and studied whether people attribute the appropriate emotion to the robot, and interact with it accordingly. In Experiment 2 we investigated whether participants tend to attribute guilty behaviour to a robot in a relevant context by examining whether relying on the robot''s greeting behaviour human participants can detect if the robot transgressed a predetermined rule. Results of Experiment 1 showed that people readily attribute emotions to a social robot and interact with it in accordance with the expressed emotional behaviour. Results of Experiment 2 showed that people are able to recognize if the robot transgressed on the basis of its greeting behaviour. In summary, our findings showed that dog-inspired behaviour is a suitable medium for making people attribute emotional states to a non-humanoid robot.     

Learning to Estimate Dynamical State with Probabilistic Population Codes

Tracking moving objects, including one’s own body, is a fundamental ability of higher organisms, playing a central role in many perceptual and motor tasks. While it is unknown how the brain learns to follow and predict the dynamics of objects, it is known that this process of state estimation can be learned purely from the statistics of noisy observations. When the dynamics are simply linear with additive Gaussian noise, the optimal solution is the well known Kalman filter (KF), the parameters of which can be learned via latent-variable density estimation (the EM algorithm). The brain does not, however, directly manipulate matrices and vectors, but instead appears to represent probability distributions with the firing rates of population of neurons, “probabilistic population codes.” We show that a recurrent neural network—a modified form of an exponential family harmonium (EFH)—that takes a linear probabilistic population code as input can learn, without supervision, to estimate the state of a linear dynamical system. After observing a series of population responses (spike counts) to the position of a moving object, the network learns to represent the velocity of the object and forms nearly optimal predictions about the position at the next time-step. This result builds on our previous work showing that a similar network can learn to perform multisensory integration and coordinate transformations for static stimuli. The receptive fields of the trained network also make qualitative predictions about the developing and learning brain: tuning gradually emerges for higher-order dynamical states not explicitly present in the inputs, appearing as delayed tuning for the lower-order states.     

Improved Reconstruction of In Silico Gene Regulatory Networks by Integrating Knockout and Perturbation Data

We performed computational reconstruction of the in silico gene regulatory networks in the DREAM3 Challenges. Our task was to learn the networks from two types of data, namely gene expression profiles in deletion strains (the ‘deletion data’) and time series trajectories of gene expression after some initial perturbation (the ‘perturbation data’). In the course of developing the prediction method, we observed that the two types of data contained different and complementary information about the underlying network. In particular, deletion data allow for the detection of direct regulatory activities with strong responses upon the deletion of the regulator while perturbation data provide richer information for the identification of weaker and more complex types of regulation. We applied different techniques to learn the regulation from the two types of data. For deletion data, we learned a noise model to distinguish real signals from random fluctuations using an iterative method. For perturbation data, we used differential equations to model the change of expression levels of a gene along the trajectories due to the regulation of other genes. We tried different models, and combined their predictions. The final predictions were obtained by merging the results from the two types of data. A comparison with the actual regulatory networks suggests that our approach is effective for networks with a range of different sizes. The success of the approach demonstrates the importance of integrating heterogeneous data in network reconstruction.     

Study on Decentralization of Spherical Amphibious Multi-robot Control System Based on Smart Contract and Blockchain

With the development of intelligent bionic robots and the improvement of military application,a single robot cannot meet the requirements of the tasks of the current era.The more complex tasks require not only that the robot be able to pass through the field barriers and the amphibious environment,but also that the robot be able to collaborate in a multi-robot system.Consequently,research on the multi-robot control system of spherical amphibious robots is very important.Pres-ently,the main research on amphibious robots is to improve the functions of a single robot,in the absence of the study of the multi-robot control system.Existing systems primarily use a centralized control methodology.Although the transfer of central node can be achieved,there is still a problem of Byzantine fault tolerance in military applications,that is,when the amphibious multi-robot system is invaded by the enemy.The central node may not only fail to accomplish the task,but also lose control of other robots,with severe consequences.To solve the above problems,this paper proposed a decentralized method of spherical amphibious multi-robot control system based on blockchain technology.First,the point-to-point informa-tion network based on long range radio technology of low power wide area network was set up,we designed the blockchain system for embedded application environment and the decentralized hardware and software architecture of multi-robot control system.On this basis,the consensus plugin,smart contract and decentralized multi-robot control algorithm were designed to achieve decentralization.The experimental results of consensus of spherical amphibious multi-robot showed the effectiveness of the decentralization.     

You Seem Certain but You Were Wrong Before: Developmental Change in Preschoolers’ Relative Trust in Accurate versus Confident Speakers

The present study tested how preschoolers weigh two important cues to a person’s credibility, namely prior accuracy and confidence, when deciding what to learn and believe. Four- and 5-year-olds (N = 96) preferred to believe information provided by a confident rather than hesitant individual; however, when confidence conflicted with accuracy, preschoolers increasingly favored information from the previously accurate but hesitant individual as they aged. These findings reveal an important developmental progression in how children use others’ confidence and prior accuracy to shape what they learn and provide a window into children’s developing social cognition, scepticism, and critical thinking.     

T. cruzi DNA polymerase beta (Tcpolβ) is phosphorylated in vitro by CK1, CK2 and TcAUK1 leading to the potentiation of its DNA synthesis activity

The unicellular protozoan Trypanosoma cruzi is the causing agent of Chagas disease which affects several millions of people around the world. The components of the cell signaling pathways in this parasite have not been well studied yet, although its genome can encode several components able to transduce the signals, such as protein kinases and phosphatases. In a previous work we have found that DNA polymerase β (Tcpolβ) can be phosphorylated in vivo and this modification activates the synthesis activity of the enzyme. Tcpolβ is kinetoplast-located and is a key enzyme in the DNA base excision repair (BER) system. The polypeptide possesses several consensus phosphorylation sites for several protein kinases, however, a direct phosphorylation of those sites by specific kinases has not been reported yet. Tcpolβ has consensus phosphorylation sites for casein kinase 1 (CK1), casein kinase 2 (CK2) and aurora kinase (AUK). Genes encoding orthologues of those kinases exist in T. cruzi and we were able to identify the genes and to express them to investigate whether or no Tcpolβ could be a substrate for in vitro phosphorylation by those kinases. Both CK1 and TcAUK1 have auto-phosphorylation activities and they are able to phosphorylate Tcpolβ. CK2 cannot perform auto-phosphorylation of its subunits, however, it was able to phosphorylate Tcpolβ. Pharmacological inhibitors used to inhibit the homologous mammalian kinases can also inhibit the activity of T. cruzi kinases, although, at higher concentrations. The phosphorylation events carried out by those kinases can potentiate the DNA polymerase activity of Tcpolβ and it is discussed the role of the phosphorylation on the DNA polymerase and lyase activities of Tcpolβ. Taken altogether, indicates that CK1, CK2 and TcAUK1 can play an in vivo role regulating the function of Tcpolβ.     

Taking practical learning in STEM education home: Examples from do‐it‐yourself experiments in plant biology

Practical teaching can give authentic learning experiences and teach valuable skills for undergraduate students in the STEM disciplines. One of the main ways of giving students such experiences, laboratory teaching, is met with many challenges such as budget cuts, increased use of virtual learning, and currently the university lockdowns due to the COVID‐19 pandemic. We highlight how at‐home do‐it‐yourself (DIY) experiments can be a good way to include physical interaction with your study organism, system, or technique to give the students a practical, authentic learning experience. We hope that by outlining the benefits of a practical, at‐home, DIY experiment we can inspire more people to design these teaching activities in the current remote teaching situation and beyond. By contributing two examples in the field of plant biology we enrich the database on experiments to draw inspiration from for these teaching methods.     

Principles of goal-directed spatial robot navigation in biomimetic models

Mobile robots and animals alike must effectively navigate their environments in order to achieve their goals. For animals goal-directed navigation facilitates finding food, seeking shelter or migration; similarly robots perform goal-directed navigation to find a charging station, get out of the rain or guide a person to a destination. This similarity in tasks extends to the environment as well; increasingly, mobile robots are operating in the same underwater, ground and aerial environments that animals do. Yet despite these similarities, goal-directed navigation research in robotics and biology has proceeded largely in parallel, linked only by a small amount of interdisciplinary research spanning both areas. Most state-of-the-art robotic navigation systems employ a range of sensors, world representations and navigation algorithms that seem far removed from what we know of how animals navigate; their navigation systems are shaped by key principles of navigation in ‘real-world’ environments including dealing with uncertainty in sensing, landmark observation and world modelling. By contrast, biomimetic animal navigation models produce plausible animal navigation behaviour in a range of laboratory experimental navigation paradigms, typically without addressing many of these robotic navigation principles. In this paper, we attempt to link robotics and biology by reviewing the current state of the art in conventional and biomimetic goal-directed navigation models, focusing on the key principles of goal-oriented robotic navigation and the extent to which these principles have been adapted by biomimetic navigation models and why.     

A debranching enzyme deficiency in endosperms of the sugary-1 mutants of maize

Many of the sugary-1 mutants of maize (Zea mays L.) have the highly branched water-soluble polysaccharide, phytoglycogen, in quantities equal to or greater than starch as an endosperm storage product in mature seeds. We find that all sugary mutants investigated are deficient in debranching enzyme [α-(1, 6)-glucosidase] activity in endosperm tissue 23 days postpollination and suggest that this deficiency is the primary biochemical lesion leading to phytoglycogen accumulation in sugary endosperms. This would indicate that the amylopectin component of starch depends on an equilibrium between the activities of branching enzymes introducing α-1,6 branch points into the linear α-1,4 glucans and debranching enzymes. The debranching enzyme activities from nonsugary endosperms can be separated into three peaks on a hydroxyapatite column. The sugary endosperm extracts lack one of these peaks of activity while the other two fractions have much reduced activity. The embryos of developing seeds (23 days after pollination) from both sugary and nonsugary genotypes have equivalent debranching activity. The debranching enzyme activity of developing endosperms is proportional to the number of copies (0 to 3) of the nonmutant (Su) allele present suggesting that the Su allele may be the structural gene for this debranching enzyme, although this is not definitive. This identification of debranching enzyme activity as being the biochemical lesion in sugary endosperms is consistent with several previous observations on the mutant.     

Assessment of medium and large‐sized mammals and their behavioral response toward anthropogenic activities in Jorgo‐Wato Protected Forest,Western Ethiopia

Medium and large‐sized mammals of Jorgo‐Wato Protected Forest have not yet been documented though the forest established before four decades. Hence, this study aims to document medium and large mammals and the behavioral responses of selected mammals toward anthropogenic activities in the study area. The study was conducted from February 2015 to June 2016, encompassing the wet and dry seasons. Data were collected mainly through camera traps, indirect and direct evidence. The study revealed about 23 medium and large‐sized mammals that belong to seven orders namely Bovidae, Carnivora, Primates, Rodentia, Tubulidentata, Lagomorpha, and Hyracoidea. Papio anubis, C. guereza, and C. aethiops were the most abundant large mammals in JWPF. Because of high anthropogenic activities, African buffalo shifted its activity period from diurnal into crepuscular and nocturnal. African buffalo traveled longer distances during the wet season (mean = 14.33 km, SD = 1.25 km) than during the dry season (mean = 9.00 km, SD = 2.16 km). This could be due to the fact that the local people were less likely to go to the forest for resource exploitation during the wet season as they are fully engaged in agricultural activities. However, low agricultural activities during the dry season allow the local people to extract resources and involve in bushmeat hunting which could limit the movement of mammals to their refugia. African buffalo preferred to rest on and adjacent to a gravel road (22.1%) in the forest, followed by on open rocky hilltops (14.7%) at night time, but rest in the bottomland thicket vegetation during the dry daytime. Regardless of high human pressure in the area, this study has revealed a good number of medium and large‐sized mammals that could be used as baseline information to design a sound conservation and management action plan of large mammals and their habitat in Jorgo‐Wato Protected Forest.     

An Efficient Gait-generating Method for Electrical Quadruped Robot Based on Humanoid Power Planning Approach

The research field of legged robots has always relied on the bionic robotic research,especially in locomotion regulating approaches,such as foot trajectory planning,body stability regulating and energy efficiency prompting.Minimizing energy consumption and keeping the stability of body are considered as two main characteristics of human walking.This work devotes to develop an energy-efficient gait control method for electrical quadruped robots with the inspiration of human walking pattern.Based on the mechanical power distribution trend,an efficient humanoid power redistribution approach is established for the electrical quadruped robot.Through studying the walking behavior acted by mankind,such as the foot trajectory and change of mechanical power,we believe that the proposed controller which includes the bionic foot movement trajectory and humanoid power redistribution method can be implemented on the electrical quadruped robot prototype.The stability and energy efficiency of the proposed controller are tested by the simulation and the single-leg prototype experi-ment.The results verify that the humanoid power planning approach can improve the energy efficiency of the electrical quadruped robots.     

Empirical Study on Shapes of the Foot Pad and Walking Gaits for Water-Running Robots

Recently, various kinds ofbiomimetic robots have been studied. Among these biomimetic robots, water-running robots that mimic the characteristics of basilisk lizards have received much attention. However, studies on the performance with respect to different geometric parameters and gaits have been lacking. To run on the surface of water, a water-running robot needs suffi- cient force with high stability to stay above the water. We experimentally measured the performance of the foot pads with different geometric parameters and with various gaits. We measured and analyzed the forces in the vertical direction and rolling angles of five different foot pad shapes： a circular shape, square shape, half-spherical shape, open half-cylinder shape, and closed half-cylinder shape. Additionally, the rolling stabilities of three kinds of gaits： biped, trotting, and tripod, were also empirically analyzed. The results of this research can be used as a guideline to design a stable water-running robot.     

Crises and Resilience at the Frontline—Public Health Facility Managers under Devolution in a Sub-County on the Kenyan Coast

Background

Public primary health care (PHC) facilities are for many individuals the first point of contact with the formal health care system. These facilities are managed by professional nurses or clinical officers who are recognised to play a key role in implementing health sector reforms and facilitating initiatives aimed at strengthening community involvement. Little in-depth research exists about the dimensions and challenges of these managers’ jobs, or on the impact of decentralisation on their roles and responsibilities. In this paper, we describe the roles and responsibilities of PHC managers–or ‘in-charges’ in Kenya, and their challenges and coping strategies, under accelerated devolution.

Methods

The data presented in this paper is part of a wider set of activities aimed at understanding governance changes under devolution in Kenya, under the umbrella of a ‘learning site’. A learning site is a long term process of collaboration between health managers and researchers deciding together on key health system questions and interventions. Data were collected through seven formal in depth interviews and observations at four PHC facilities as well as eight in depth interviews and informal interactions with sub-county managers from June 2013 to July 2014. Drawing on the Aragon framework of organisation capacity we discuss the multiple accountabilities, daily routines, challenges and coping strategies among PHC facility managers.

Results

PHC in-charges perform complex and diverse roles in a difficult environment with relatively little formal preparation. Their key concerns are lack of job clarity and preparedness, the difficulty of balancing multidirectional accountability responsibilities amidst significant resource shortages, and remuneration anxieties. We show that day-to-day management in an environment of resource constraints and uncertainty requires PHC in-charges who are resilient, reflective, and continuously able to learn and adapt. We highlight the importance of leadership development including the building of critical soft skills such as relationship building.     

Restructuring and Health in Canadian Coastal Communities

Environmental and socioeconomic restructuring has had profound consequences for coastal communities in Canada. The decline of traditional resource-based industries—fisheries, forestry, and mining—and the emergence of new economic activities, such as tourism and aquaculture, compounded by concurrent shifts in social programs, have affected the health of environments, communities, and people. Drawing on research conducted as part of the interdisciplinary major collaborative research initiative Coasts Under Stress, we examined the implications of interactive restructuring for the health of people and communities on Canada’s east and west coasts. The research is guided by a socioecological framework that identifies the pathways from interactive restructuring through health determinants to health risks and health outcomes. The utility of the proposed framework is exemplified by a specific place-based example in Prince Rupert, British Columbia, and a case-based example from coastal communities in Newfoundland and Labrador. A focus on interactive restructuring draws our attention to the many challenges associated with promoting health in a context of rapid and often accelerating environmental and institutional change that is relevant to other areas and contexts.     

Why Robots Should Be Social: Enhancing Machine Learning through Social Human-Robot Interaction

Social learning is a powerful method for cultural propagation of knowledge and skills relying on a complex interplay of learning strategies, social ecology and the human propensity for both learning and tutoring. Social learning has the potential to be an equally potent learning strategy for artificial systems and robots in specific. However, given the complexity and unstructured nature of social learning, implementing social machine learning proves to be a challenging problem. We study one particular aspect of social machine learning: that of offering social cues during the learning interaction. Specifically, we study whether people are sensitive to social cues offered by a learning robot, in a similar way to children’s social bids for tutoring. We use a child-like social robot and a task in which the robot has to learn the meaning of words. For this a simple turn-based interaction is used, based on language games. Two conditions are tested: one in which the robot uses social means to invite a human teacher to provide information based on what the robot requires to fill gaps in its knowledge (i.e. expression of a learning preference); the other in which the robot does not provide social cues to communicate a learning preference. We observe that conveying a learning preference through the use of social cues results in better and faster learning by the robot. People also seem to form a “mental model” of the robot, tailoring the tutoring to the robot’s performance as opposed to using simply random teaching. In addition, the social learning shows a clear gender effect with female participants being responsive to the robot’s bids, while male teachers appear to be less receptive. This work shows how additional social cues in social machine learning can result in people offering better quality learning input to artificial systems, resulting in improved learning performance.     

Robots with Display Screens: A Robot with a More Humanlike Face Display Is Perceived To Have More Mind and a Better Personality

It is important for robot designers to know how to make robots that interact effectively with humans. One key dimension is robot appearance and in particular how humanlike the robot should be. Uncanny Valley theory suggests that robots look uncanny when their appearance approaches, but is not absolutely, human. An underlying mechanism may be that appearance affects users’ perceptions of the robot’s personality and mind. This study aimed to investigate how robot facial appearance affected perceptions of the robot’s mind, personality and eeriness. A repeated measures experiment was conducted. 30 participants (14 females and 16 males, mean age 22.5 years) interacted with a Peoplebot healthcare robot under three conditions in a randomized order: the robot had either a humanlike face, silver face, or no-face on its display screen. Each time, the robot assisted the participant to take his/her blood pressure. Participants rated the robot’s mind, personality, and eeriness in each condition. The robot with the humanlike face display was most preferred, rated as having most mind, being most humanlike, alive, sociable and amiable. The robot with the silver face display was least preferred, rated most eerie, moderate in mind, humanlikeness and amiability. The robot with the no-face display was rated least sociable and amiable. There was no difference in blood pressure readings between the robots with different face displays. Higher ratings of eeriness were related to impressions of the robot with the humanlike face display being less amiable, less sociable and less trustworthy. These results suggest that the more humanlike a healthcare robot’s face display is, the more people attribute mind and positive personality characteristics to it. Eeriness was related to negative impressions of the robot’s personality. Designers should be aware that the face on a robot’s display screen can affect both the perceived mind and personality of the robot.