Human-like Robot Research Articles

Advice from Robots: Would You Choose a Robot that Looked More or Less Human?

Humans are increasingly turning to non-human agents for advice. Therefore, it is important to investigate if human-likeness of a robot affects advice-seeking. In this experiment, participants chose robot advisors with different levels of human-likeness when completing either social or analytical tasks, and the task was either known or unknown when the robot advisor was selected. In the agent first condition, participants chose the advisor before receiving their task assignment, and in the task first condition participants received their task assignment before choosing the advisor. Results indicated that task type did not play a role in agent selection in either condition. However, in the agent first condition, more human-like robots (Nao and Kodomoroid) were selected at a higher rate than machine-like robots (Cozmo) and, in the task first condition, Nao was selected at a higher rate than Cozmo or Kodomoroid. These results should be considered when designing robots for giving advice to improve human-robot interaction.

Robots facilitate human language production

Despite recent developments in integrating autonomous and human-like robots into many aspects of everyday life, social interactions with robots are still a challenge. Here, we focus on a central tool for social interaction: verbal communication. We assess the extent to which humans co-represent (simulate and predict) a robot’s verbal actions. During a joint picture naming task, participants took turns in naming objects together with a social robot (Pepper, Softbank Robotics). Previous findings using this task with human partners revealed internal simulations on behalf of the partner down to the level of selecting words from the mental lexicon, reflected in partner-elicited inhibitory effects on subsequent naming. Here, with the robot, the partner-elicited inhibitory effects were not observed. Instead, naming was facilitated, as revealed by faster naming of word categories co-named with the robot. This facilitation suggests that robots, unlike humans, are not simulated down to the level of lexical selection. Instead, a robot’s speaking appears to be simulated at the initial level of language production where the meaning of the verbal message is generated, resulting in facilitated language production due to conceptual priming. We conclude that robots facilitate core conceptualization processes when humans transform thoughts to language during speaking.

Reducing the uncanny valley by dehumanizing humanoid robots

Humanoid robots are often experienced as unnerving, a psychological phenomenon called the “uncanny valley.” Past work reveals that humanlike robots are unnerving in part because they are ascribed humanlike feelings. We leverage this past work to provide a potential solution to the uncanny valley. Three studies reveal that “dehumanizing” humanoid robots—stripping robots of their apparent capacity for feelings—can significantly reduce the uncanny valley. Participants high on trait dehumanization (Study 1) or experimentally instructed to dehumanize (Study 2) reported lower feelings of uncanniness when viewing a humanoid robot, an effect mediated by reduced perceptions of feelings. We replicate these effects in an experimental field study where hotel guests interacted with real humanoid robots in Japan, and reveal that dehumanization reduces the uncanny valley without decreasing customers’ satisfaction (Study 3).

Mind the Robot! Variation in Attributions of Mind to a Wide Set of Real and Fictional Robots

The rapid rise of computing power has prompted the desire to develop more social, human-like robots. Quantitatively comparing different computing systems on their ability to simulate human qualities has been a major technical challenge. A recent framework put forth by Gray et al. (Science 315(5812):619, 2007. https://doi.org/10.1126/science.1134475) provides promise as a new means for comparing robots. While the framework has been validated for assessing individual robots with different descriptions, or different behaviours, the framework has not been applied to a wider landscape of robots and machines situated amongst other characters. The present study sought to investigate attributions of mind towards a wide range of real and fictional robots. We asked participants to rate the agency (the ability “to do”) and experience (the ability “to feel”) of 24 characters made up of humans, robots, inanimate objects, and animals. Although robots were collectively rated lower than humans on agency and experience, there was significant variation among robots—even when fictional robots were omitted. The results of this investigation suggest that building robots that are perceived to feel is a fruitful avenue for future development as people are more open to perceiving aspects of mind in a wider range of robots than previously established. Our results also indicate that age is a critical factor in people’s attributions of mind to robots, suggesting that there may be a generational shift towards greater acceptance of robots’ ability to both do and feel.

Promises and trust in human\u2013robot interaction

Understanding human trust in machine partners has become imperative due to the widespread use of intelligent machines in a variety of applications and contexts. The aim of this paper is to investigate whether human-beings trust a social robot—i.e. a human-like robot that embodies emotional states, empathy, and non-verbal communication—differently than other types of agents. To do so, we adapt the well-known economic trust-game proposed by Charness and Dufwenberg (2006) to assess whether receiving a promise from a robot increases human-trust in it. We find that receiving a promise from the robot increases the trust of the human in it, but only for individuals who perceive the robot very similar to a human-being. Importantly, we observe a similar pattern in choices when we replace the humanoid counterpart with a real human but not when it is replaced by a computer-box. Additionally, we investigate participants’ psychophysiological reaction in terms of cardiovascular and electrodermal activity. Our results highlight an increased psychophysiological arousal when the game is played with the social robot compared to the computer-box. Taken all together, these results strongly support the development of technologies enhancing the humanity of robots.

On the Gap between Domestic Robotic Applications and Computational Intelligence

Aspired to build intelligent agents that can assist humans in daily life, researchers and engineers, both from academia and industry, have kept advancing the state-of-the-art in domestic robotics. With the rapid advancement of both hardware (e.g., high performance computing, smaller and cheaper sensors) and software (e.g., deep learning techniques and computational intelligence technologies), robotic products have become available to ordinary household users. For instance, domestic robots have assisted humans in various daily life scenarios to provide: (1) physical assistance such as floor vacuuming; (2) social assistance such as chatting; and (3) education and cognitive assistance such as offering partnerships. Crucial to the success of domestic robots is their ability to understand and carry out designated tasks from human users via natural and intuitive human-like interactions, because ordinary users usually have no expertise in robotics. To investigate whether and to what extent existing domestic robots can participate in intuitive and natural interactions, we survey existing domestic robots in terms of their interaction ability, and discuss the state-of-the-art research on multi-modal human–machine interaction from various domains, including natural language processing and multi-modal dialogue systems. We relate domestic robot application scenarios with state-of-the-art computational techniques of human–machine interaction, and discuss promising future directions towards building more reliable, capable and human-like domestic robots.

Evaluation of Robot Professor Technology in Teaching and Business

In this paper the Robot technology development will be evaluated in teaching and business. Impacts hypothesis of robot’s applications are discussed in addition to the types of the robots with time line. The results of this study show the advantages and disadvantages of using the Robot teacher in classroom and business and it shows that the Robot teacher performance is not like the human teacher due to the senses that owned by human being. Robot impacts minimizes the employment worldwide and it is predicted that around 800 million of global workers will lose their jobs in 2030. The results show that impacts hypothesis is minimized through improving the performance of robot technology by using AI Artificial Intelligence, also the Robot applications in the global health system have contributed effectively and successfully to prevention of the spread of the coronavirus pandemic. The Sophia Robot with AI improvements, behaves with good feelings, senses and debating friendly and it is approved by UNDP on 2018. In this study, it is concluded that the robot is not like human being, although developing human-like robot is still out of reach. Also, AI is the best tool for enhancing the Robot technology and increase the employment involvement rate in near future, which is one of the future challenges and studies proposed in this research, industry 4.

Social Robots: A Bridge Between the Two Cultures

This paper aims to show the possible and actual synergies between social robotics and sociology. The author argues that social robots are one of the best fields of inquiry to provide a bridge between the two cultures — the one represented by the social sciences and the humanities on the one hand, and the one represented by the natural sciences and engineering on the other. To achieve this result, quantitative and qualitative analyses are implemented. By using scientometric tools like Ngram Viewer, search engines such as Google Scholar, and hand calculations, the author detects the emergence of the term-and-concept ‘social robots’ in its current use, the absolute and relative frequencies of this term in the scientific literature in the period 1800–2008, the frequency distribution of publications including this term in the period 2000–2019, and the magnitude of publications in which the term ‘social robots’ is associated to the term ‘sociology’ or ’social work’. Finally, employing qualitative analysis and focusing on exemplary cases, this paper shows different ways of implementing researches that relate sociology to robotics, from a theoretical or instrumental point of view. It is argued that sociologists and engineers could work in a team to observe, analyze, and describe the interaction between humans and social robots, by using research techniques and theoretical frames provided by sociology. In turn, this knowledge can be used to build more effective and humanlike social robots.
 Keywords: social robots, sociology, social work, meta-analysis, scientometrics

Formation of clinical thinking with the use of human-like robots

Due to the fact that students rarely take part in diagnostic and therapeutic activities, there is a huge gap in the practical medical practice. To replenish it, there is the possibility of introducing humanoid robots with various scenarios into the educational process for the correct formation of professional skills and clinical thinking of students and young doctors.

Are friends electric? The benefits and risks of human-robot relationships.

SummarySocial robots that can interact and communicate with people are growing in popularity for use at home and in customer-service, education, and healthcare settings. Although growing evidence suggests that co-operative and emotionally aligned social robots could benefit users across the lifespan, controversy continues about the ethical implications of these devices and their potential harms. In this perspective, we explore this balance between benefit and risk through the lens of human-robot relationships. We review the definitions and purposes of social robots, explore their philosophical and psychological status, and relate research on human-human and human-animal relationships to the emerging literature on human-robot relationships. Advocating a relational rather than essentialist view, we consider the balance of benefits and harms that can arise from different types of relationship with social robots and conclude by considering the role of researchers in understanding the ethical and societal impacts of social robotics.

The Eternal Robot: Anchoring Effects in Humans' Mental Models of Robots and Their Self.

Current robot designs often reflect an anthropomorphic approach, apparently aiming to convince users through an ideal system, being most similar or even on par with humans. The present paper challenges human-likeness as a design goal and questions whether simulating human appearance and performance adequately fits into how humans think about robots in a conceptual sense, i.e., human's mental models of robots and their self. Independent of the technical possibilities and limitations, our paper explores robots' attributed potential to become human-like by means of a thought experiment. Four hundred eighty-one participants were confronted with fictional transitions from human-to-robot and robot-to-human, consisting of 20 subsequent steps. In each step, one part or area of the human (e.g., brain, legs) was replaced with robotic parts providing equal functionalities and vice versa. After each step, the participants rated the remaining humanness and remaining self of the depicted entity on a scale from 0 to 100%. It showed that the starting category (e.g., human, robot) serves as an anchor for all former judgments and can hardly be overcome. Even if all body parts had been exchanged, a former robot was not perceived as totally human-like and a former human not as totally robot-like. Moreover, humanness appeared as a more sensible and easier denied attribute than robotness, i.e., after the objectively same transition and exchange of the same parts, the former human was attributed less humanness and self left compared to the former robot's robotness and self left. The participants' qualitative statements about why the robot has not become human-like, often concerned the (unnatural) process of production, or simply argued that no matter how many parts are exchanged, the individual keeps its original entity. Based on such findings, we suggest that instead of designing most human-like robots in order to reach acceptance, it might be more promising to understand robots as an own “species” and underline their specific characteristics and benefits. Limitations of the present study and implications for future HRI research and practice are discussed.

“He’s a Machine—made So.”: Rethinking Humanlike Robots in Issac Asimov’s I, Robot

While humans in the twenty-first century are still troubled by controversial issues such as race, gender, and class, a non-human movement has been restlessly advancing in the past few years, prompt...

Exploring the Effect of Virtual Robot Acceleration on Perceived Competitiveness/ Cooperativeness, Animacy, and Intelligence

Prior research has indicated that perception of acceleration may be a direct process. This direct process may be conceptually linked to the ecological approach to visual perception and a further extension of direct social perception. The present study examines the effects of perception of acceleration in virtual reality on participants’ perceived attributes (perceived intelligence and animacy) of a virtual human-like robot agent and perceived agent competitive/cooperativeness. Perceptual judgments were collected after experiencing one of the five different conditions dependent on the participant’s acceleration: mirrored acceleration, faster acceleration, slowed acceleration, varied acceleration resulting in a win, and varied acceleration resulting in a loss. Participants experienced each condition twice in a counterbalanced fashion. The focus of the experiment was to determine whether different accelerations influenced perceptual judgments of the observers. Results suggest that faster acceleration was perceived as more competitive and slower acceleration was reported as low in animacy and perceived intelligence.

Optimal robot for intervention for individuals with autism spectrum disorders.

With recent rapid advances in technology, human‐like robots have begun functioning in a variety of ways. As increasing anecdotal evidence suggests, robots may offer many unique opportunities for helping individuals with autism spectrum disorders (ASD). Individuals with ASD often achieve a higher degree of task engagement through the interaction with robots than through interactions with human trainees. The type and form of robots to be used for individuals with ASD have been meticulously considered. Simple robots and animal robots are acceptable because of their simplicity and the ease of interesting and engaging interactions. Android robots have the benefit of the potential of generalization into daily life to some extent. Considering the affinity between robots and users is important to draw out the potential capabilities of robotic intervention to the fullest extent. In the robotic condition, factors such as the appearance, biological motion, clothes, hairstyle, and disposition are important. Many factors of a user, such as age, sex, and IQ, may also affect the affinity of individuals with ASD toward a robot. The potential end‐users of this technology may be unaware or unconvinced of the potential roles of robots in ASD interventions. If trainers have extensive experience in using robots, they can identify many potential roles of robots based on their experience. To date, only a few studies have been conducted in the field of robotics for providing assistance to individuals with ASD, and future studies are needed to realize an optimal robot for this purpose.

“Intimate relationship” with “virtual humans” and the “socialification” of familyship

AbstractIn this article, the author provides a new view on intimate relationships with “virtual humans” such as robots and AIs. Recent technological developments have enabled firms to create humanlike robots and AIs. It is likely that, in the near future, a growing number of people will want intimate relationships with these virtual humans. This may receive harsh criticism, for example, that such a move would indicate objectification of women or ethical wrongdoing. Instead, however, it should be viewed in light of quality of life (QOL) for sexual minorities and people with various difficulties. Aided by the discussion of the introduction of the Long Term Public Care Insurance (LTCI) system in Japan in the late 1990s, the author positions this trend as the “socialification” of familyship – that is, a phenomenon in which the virtual humans, as products or services offered by businesses, become partners/family members, and a change by which some parts of the intimate relationships within families are shared in society. Just as the LTCI system, which was introduced as a socialification of nursing care, reduced the burden of care on Japanese women and improved their QOL, adoption of virtual humans as a socialification of familyship is also likely to improve the QOL of people with difficulties worldwide.

Dynamical attitude configuration with wearable wireless body sensor networks through beetle antennae search strategy

Wearable wireless body sensor networks (WWBSN) are helpful to the dynamical attitude configuration of human-like robots. And then, we employ the Beetle Antennae Search (BAS) algorithm for a field robot to obtain an optimal solution to both path planning and attitude configuration by using the measurements of WWBSN. In terms of obstacle avoidance further more, the proposed Beetle Antennae Search for Attitude Optimization (BASAO) of manipulator develops two important mechanisms, including both experience-oriented part and inner loop step self-attenuate part, to enhance the efficiency and accuracy. earch efficiency is improved by fusing the target information via guidance strategy, and the optimization of each iteration is coordinated by local self-decaying step size. For manipulators mounted with WWBSN, the proposed method is implemented in three-dimensional attitude planning simulations, some of which consider a challenging obstacle situation. Simulation results show that the proposed BASAO algorithm is effective in the attitude planning with WWBSN, compared with the traditional Rapidly-exploring Random Tree (RRT) algorithm.

Hiroshi Ishiguro: Android science and 'upstream engagement'

Abstract This article discusses Hiroshi Ishiguro, a Japanese celebrity roboticist internationally acclaimed for his creation of androids. While his anthropomorphic machines are intended as models for future human-like robots, participating in work and domestic contexts, Ishiguro also regards them as experimental tools for investigations into questions of human identity. Beyond engineering challenges, he is not afraid to ask philosophical questions, such as 'what is the human?' Ishiguro has even had facial plastic surgery to match the appearance of his robot double, Geminoid HI-1. He has been described as the bad boy of Japanese robotics, an eccentric genius who is recognized as such in Japan, and overseas. While Ishiguro conducts scientific experiments, he has also deployed his anthropomorphic robots in popular entertainment contexts such as film, television, theatre and in museum exhibitions. Although Ishiguro's androids have almost always been included in mainstream western journalism's coverage concerning the development of next-generation robots in Japan, his anthropomorphic machines are often shown along with a photo of Ishiguro in his trademark black clothing, and described as 'freaky' and 'creepy'. I argue that Ishiguro's presentation feeds the western fascination with Japanese robot technology. This article examines the relationship between Ishiguro's larger-than-life public persona and his philosophy concerning his work as a kind of storytelling and upstream engagement in the context of robotic science.

An ontic–ontological theory for ethics of designing social robots: a case of Black African women and humanoids

Given the affective psychological and cognitive dynamics prevalent during human–robot-interlocution, the vulnerability to cultural-political influences of the design aesthetics of a social humanoid robot has far-reaching ramifications. Building upon this hypothesis, I explicate the relationship between the structures of the constitution social ontology and computational semiotics, and ventures a theoretical framework which I proposes as a thesis that impels a moral responsibility on engineers of social humanoids. In distilling this thesis, the implications of the intersection between the socio-aesthetics of racialised and genderised humanoids and the phenomenology of human–robot-interaction are illuminated by the figuration of the experience of a typical black rural African woman as the user, that is, an interlocutor with an industry-standard socially-situated humanlike robot. The representation of the gravity of the psycho-existential and socio-political ramifications of such woman’s life with humanoids is abstracted and posited as grounds that illustrate the imperative for roboticists to take socio-ethical considerations seriously in their designs of humanoids.

EEG Classification of Forearm Movement Imagery Using a Hierarchical Flow Convolutional Neural Network

Recent advances in brain-computer interface (BCI) techniques have led to increasingly refined interactions between users and external devices. Accurately decoding kinematic information from brain signals is one of the main challenges encountered in the control of human-like robots. In particular, although the forearm of an upper extremity is frequently used in daily life for high-level tasks, only few studies addressed decoding of the forearm movement. In this study, we focus on the classification of forearm movements according to elaborated rotation angles using electroencephalogram (EEG) signals. To this end, we propose a hierarchical flow convolutional neural network (HF-CNN) model for robust classification. We evaluate the proposed model not only with our experimental dataset but also with a public dataset (BNCI Horizon 2020). The grand-average classification accuracies of three rotation angles yield 0.73 (±0.04) for the motor execution (ME) task and 0.65 (±0.09) for the motor imagery (MI) task across ten subjects in our experimental dataset. Further, in the public dataset, the grand-averaged classification accuracies were 0.52 (±0.03) for ME and 0.51 (±0.04) for MI tasks across fifteen subjects. Our experimental results demonstrate the possibility of decoding complex kinematics information using EEG signals. This study will contribute to the development of a brain-controlled robotic arm system capable of performing high-level tasks.

Automatic domain modeling for human–robot interaction

This paper introduces an approach to automatic domain modeling for human–robot interaction. The proposed approach is symbolic and intended for semantically unconstrained task-oriented human–robot interaction domains. At the specification level, it is cognitively inspired, addressing selected cognitive mechanisms of the human memory system (e.g., integration, semantic categorization, associative learning, etc.) that are relevant for natural language human–robot interaction. We discuss a corpus-based validation of the introduced approach and report on its particular implementation within the conversational agent integrated with a human-like robot.