Robot Mind Research Articles

Evolution of the “4-D Approach” to Dynamic Vision for Vehicles

Spatiotemporal models for the 3-D shape and motion of objects allowed large progress in the 1980s in visual perception of moving objects observed from a moving platform. Despite the successes demonstrated with several vehicles, the “4-D approach” has not been accepted generally. Its advantage is that only the last image of the sequence needs to be analyzed in detail to allow the full state vectors of moving objects, including their velocity components, to be reconstructed by the feedback of prediction errors. The vehicle carrying the cameras can, thus, together with conventional measurements, directly create a visualization of the situation encountered. In 1994, at the final demonstration of the project PROMETHEUS, two sedan vehicles using this approach were the only ones worldwide capable of driving autonomously in standard heavy traffic on three-lane Autoroutes near Paris at speeds up to 130 km/h (convoy driving, lane changes, passing). Up to ten vehicles nearby could be perceived. In this paper, the three-layer architecture of the perception system is reviewed. At the end of the 1990s, the system evolved from mere recognition of objects in motion, to understanding complex dynamic scenes by developing behavioral capabilities, like fast saccadic changes in the gaze direction for flexible concentration on objects of interest. By analyzing motion of objects over time, the situation for decision making was assessed. In the third-generation system “EMS-vision” behavioral capabilities of agents were represented on an abstract level for characterizing their potential behaviors. These maneuvers form an additional knowledge base. The system has proven capable of driving in networks of minor roads, including off-road sections, with avoidance of negative obstacles (ditches). Results are shown for road vehicle guidance. Potential transitions to a robot mind and to the now-favored CNN are touched on.

Avoiding the Abject and Seeking the Script: Perceived Mind, Morality, and Trust in a Persuasive Social Robot

Social robots are being groomed for human influence, including the implicit and explicit persuasion of humans. Humanlike characteristics are understood to enhance robots’ persuasive impact; however, little is known of how perceptions of two key human capacities—mind and morality—function in robots’ persuasive potential. This experiment tests the possibility that perceived robot mind and morality will correspond with greater persuasive impact, moderated by relational trustworthiness for a moral appeal and by capacity trustworthiness for a logical appeal. Via an online survey, a humanoid robot asks participants to help it learn to overcome CAPTCHA puzzles to access important online spaces—either on grounds that it is logical or moral to do so. Based on three performance indicators and one self-report indicator of compliance, analysis indicates that (a) seeing the robot as able to perceive and act on the world selectively improves compliance, and (b) perceiving agentic capacity diminishes compliance, though capacity trustworthiness can moderate that reduction. For logical appeals, social-moral mental capacities promote compliance, moderated by capacity trustworthiness. Findings suggest that, in this compliance scenario, the accessibility of schemas and scripts for engaging robots as social-moral actors may be central to whether/how perceived mind, morality, and trust function in machine persuasion.

Improving evaluations of advanced robots by depicting them in harmful situations

Equipping robots with sophisticated mental abilities can result in reduced likeability (uncanny valley of mind). Other work shows that exposing robots to harm increases empathy and likeability. Connecting both lines of research, we hypothesized that eliciting empathy could mitigate or even reverse the negative response to robots with mind. In two online experiments, we manipulated the attributes of a robot (with or without mind) and presented the robot in situations in which it was either exposed to harm or not. Perceived empathy for the robot and robot likeability served as dependent variables. Experiment 1 (N = 559) used text vignettes to manipulate robot mind and a video that involved either physical harm or no harm to the machine. In a second experiment (N = 396), both experimental factors were manipulated via the shown video. Across both experiments, we observed a significant indirect effect of presenting the robot in a harmful situation on likeability, with empathy serving as a mediating variable. Moreover, a residual negative influence of showing the robot in a harmful situation was detected. We conclude that the uncanny valley of mind observed in our studies could be based on the robot's human-like imperfection, rather than descriptions of its supposed mind.

Pay Them No Mind: the Influence of Implicit and Explicit Robot Mind Perception on the Right to be Protected

Mind perception is a fundamental part of anthropomorphism and has recently been suggested to be a dual process. The current research studied the influence of implicit and explicit mind perception on a robot’s right to be protected from abuse, both in terms of participants condemning abuse that befell the robot as well as in terms of participants’ tendency to humiliate the robot themselves. Results indicated that acceptability of robot abuse can be manipulated through explicit mind perception, yet are inconclusive about the influence of implicit mind perception. Interestingly, explicit attribution of mind to the robot did not make people less likely to mistreat the robot. This suggests that the relationship between a robot’s perceived mind and right to protection is far from straightforward, and has implications for researchers and engineers who want to tackle the issue of robot abuse.

Robots are friends as well as foes: Ambivalent attitudes toward mindful and mindless AI robots in the United States and China

In light of the ongoing and rapid development of innovative technologies, two intriguing issues arise: do people have more positive or more negative attitudes toward robots with high (versus low) mental capabilities, and do attitudes toward robots differ between Western and East Asian cultures? Past work on these topics has produced contradictory results. Inspired by the perspective that attitudes are ambivalent rather than bipolar, we argue that these controversial findings stem from people's ambivalent attitudes toward robots. To test the assumption that ambivalent attitudes toward robots differ by type of robots and by cultural background, we conducted an experimental study. By manipulating the level of robot mind and recruiting both American and Chinese participants, we examined how robot mind and culture influence ambivalent attitudes toward robots. We simultaneously measured participants' perceptions of robots as “ally” or “enemy”. The results revealed that robots with high (versus low) mental abilities elicited more ambivalent attitudes and that American participants reported more ambivalence toward robots than Chinese participants. These findings enhance our understanding of human–robot interaction and provide guidance for modulating people's attitudes toward robots.

Simplified Pain Matrix Method for Artificial Pain Activation Embedded into Robot Framework

Physical interaction in Human Robot Interaction is a predominant factor to determine the existence of collaboration between humans and robots. However, a prolonged physical interaction may lead humans to experience what is called as pain which possibly arises due to uncoordinated movements between humans and robots, internal humans’ physical constraints or other feasible causes. This paper introduces a novel method,simplified Pain Matrix (PM), which a PM to generate and activate the kinds of synthetic pain embedded into a robot framework with simplifications on some features of the Pain Matrix. Two scenarios of experiments are constructed and dedicated to demonstrate the activation procedure of the proposed pain mechanism, and the results show a reliable outcome by accurately invoking a particular synthetic pain based on stimulus obtained from the robot joint sensor, which is then followed by reactive prevention actions generated by the Robot Mind.

Android Pretending to Have Similar Traits of Imagination as Humans Evokes Stronger Perceived Capacity to Feel.

The perception of robots as mindful enriches how humans relate to them. Given that congruence in perceived representations of the world enable humans to experience commonality in mental states (a shared reality), we propose that congruence between humans, and robots will encourage humans to attribute humanlike mental capacities to robots. To investigate this, we assessed the mental perceptions of a robot in a visual imagination task using Gray et al. mind perception scale, which evaluates experience (capacity to feel), and agency (capacity to plan and do). For each ambiguous picture in the designed task, humans, and a robot imagined an animal. The task was performed under six conditions (2 × 3: Lead/Follow for Low/Medium/High). In the Lead condition, the robot records its perceived animal first; in the Follow condition, the robot records after the human participant. The experiment had three different degrees of congruence: Low (0%), Medium (60%), and High (100%). The results showed that perceived experiences were higher in the Lead condition, suggesting that the robot is perceived to be empathetic. It is probable that the Follow condition was perceived as mimicry rather than shared reality. Therefore, the order of response may have played an important role in commonality in mental states. No differences were observed in the perceived agency across all conditions. These results suggest that the order of response affects how humans perceive the minds of robots. Additionally, we assessed a post-task questionnaire to evaluate the interpersonal closeness that the humans felt toward the android. The main effect was observed in the degrees of congruence. This result is in line with those of previous studies that report relationships across sharing of similarities and friendliness.

Theory of Robot Mind: False Belief Attribution to Social Robots in Children With and Without Autism.

This study aims to probe how children with and without autism spectrum disorders (ASD) attribute false belief to a social robot and predict its action accordingly. Twenty 5- to 7-year-old children with ASD and 20 age- and IQ-matched typically developing (TD) children participated in two false belief tasks adapted for robot settings (change-of-location task and the unexpected-contents task). The results showed that most TD children are capable of attributing false belief to the social robot, that is, they could infer higher level mental states in robots, which extends our understanding in TD children’s perception and cognition on social robots. Conversely, children with ASD still show difficulty in interpreting robots’ mental states compared to their TD peers, which would greatly interfere with their interactions and communications with social robots and might impact on efficiency of robot-based intervention and education approaches. This group difference in attributing false belief to social robots could not be explained by the different perception and categorization of the robot. Our study implies that although children with ASD appear to be highly attracted by social robots, they still have difficulty in understanding mental states when socially interacting with robots, which should be taken into consideration when designing the robot-based intervention approach targeting to improve social behaviors of ASD.

The uncanny of mind in a machine: Humanoid robots as tools, agents, and experiencers

The uncanny valley hypothesis suggests that a high (but not perfect) human-likeness of robots is associated with feelings of eeriness. We distinguished between experience and agency as psychological representations of human-likeness. In four online experiments, vignettes about a new generation of robots were presented. The results indicate that a robot's capacity to feel (experience) elicits stronger feelings of eeriness than a robot's capacity to plan ahead and to exert self-control (agency, Experiment 1A), which elicits more eeriness than a robot without mind (robot as tool, Experiments 1A and 1B). This effect was attenuated when the robot was introduced to operate in a nursing environment (Experiment 2). A robot's ascribed gender did not influence the difference between the eeriness of robots introduced as experiencers, agents, or tools (Experiment 3). Additional analyses yielded some evidence for a non-linear (quadratic) effect of participants' age on the robot mind effects.

Artificial Pain May Induce Empathy, Morality, and Ethics in the Conscious Mind of Robots

In this paper, a working hypothesis is proposed that a nervous system for pain sensation is a key component for shaping the conscious minds of robots (artificial systems). In this article, this hypothesis is argued from several viewpoints towards its verification. A developmental process of empathy, morality, and ethics based on the mirror neuron system (MNS) that promotes the emergence of the concept of self (and others) scaffolds the emergence of artificial minds. Firstly, an outline of the ideological background on issues of the mind in a broad sense is shown, followed by the limitation of the current progress of artificial intelligence (AI), focusing on deep learning. Next, artificial pain is introduced, along with its architectures in the early stage of self-inflicted experiences of pain, and later, in the sharing stage of the pain between self and others. Then, cognitive developmental robotics (CDR) is revisited for two important concepts—physical embodiment and social interaction, both of which help to shape conscious minds. Following the working hypothesis, existing studies of CDR are briefly introduced and missing issues are indicated. Finally, the issue of how robots (artificial systems) could be moral agents is addressed.

Creepiness Creeps In: Uncanny Valley Feelings Are Acquired in Childhood.

The uncanny valley posits that very human-like robots are unsettling, a phenomenon amply demonstrated in adults but unexplored in children. Two hundred forty 3- to 18-year-olds viewed one of two robots (machine-like or very human-like) and rated their feelings toward (e.g., "Does the robot make you feel weird or happy?") and perceptions of the robot's capacities (e.g., "Does the robot think for itself?"). Like adults, children older than 9 judged the human-like robot as creepier than the machine-like robot-but younger children did not. Children's perceptions of robots' mental capacities predicted uncanny feelings: children judge robots to be creepy depending on whether they have human-like minds. The uncanny valley is therefore acquired over development and relates to changing conceptions about robot minds.

Does the Robot Have a Mind? Mind Perception and Attitudes Towards Robots Predict Use of an Eldercare Robot

Robots are starting to be developed for aged care populations and some of these have been made into commercial products that have been well received. However, little is known about the psychological factors that promote acceptance or rejection of robots by older people. Finding out more about these psychological determinants of robot uptake and acceptance is the primary focus of the study described in this paper. A healthcare robot feasibility study was conducted in a retirement village. Older people (n=25) were invited to use a prototype robot with healthcare functions over a two week period. Questionnaires were completed before and after the period. It was found that residents who held significantly more positive attitudes towards robots, and perceived robot minds to have less agency (ability to do things) were more likely to use the robot. It was also found that attitudes towards robots improved over time in robot-users. Our results suggest that the cognitions older people hold about robots may influence their decisions to use robots. The study results also validate participants’ subjective self-reports of attitudes towards robots and perceptions of robot mind, against the objective measure of robot use. Interventions to foster adaptive cognitions could be developed and applied in the design, deployment and marketing of robots to promote their use and acceptance.

Robot minds and human ethics: the need for a comprehensive model of moral decision making

Building artificial moral agents (AMAs) underscores the fragmentary character of presently available models of human ethical behavior. It is a distinctly different enterprise from either the attempt by moral philosophers to illuminate the "ought" of ethics or the research by cognitive scientists directed at revealing the mechanisms that influence moral psychology, and yet it draws on both. Philosophers and cognitive scientists have tended to stress the importance of particular cognitive mechanisms, e.g., reasoning, moral sentiments, heuristics, intuitions, or a moral grammar, in the making of moral decisions. However, assembling a system from the bottom-up which is capable of accommodating moral considerations draws attention to the importance of a much wider array of mechanisms in honing moral intelligence. Moral machines need not emulate human cognitive faculties in order to function satisfactorily in responding to morally significant situations. But working through methods for building AMAs will have a profound effect in deepening an appreciation for the many mechanisms that contribute to a moral acumen, and the manner in which these mechanisms work together. Building AMAs highlights the need for a comprehensive model of how humans arrive at satisfactory moral judgments.

FIVE STEPS OF EVOLUTION FROM NON-LIFE TO LIFE-LIKE ROBOTS

There are many interests in developing life-like robots, or robots which are both intelligent and autonomous. And, one obvious characteristics of life-like creatures is that they can autonomously develop and learn during their life span. Such abilities obviously depend on the ways of designing human-like minds. Then, a fundamental question is how to devise the innate, or built-in, principles behind the blueprint of a human-like mind, and to apply these findings to guide the design of the mind of life-like robots. In the literature, there are two schools of thoughts. One advocates the study of the nervous systems of biological brains (e.g. human brain) until the discovery of the blueprint of a mind. The second approach is to follow the path of invention and validation until the full understanding of physical principles which enable the design of an artificial mind that is as good as a biological mind. This paper embraces the second approach, and aims at formulating a new ground which could guide the design of the minds of life-like robots at various stages. In particular, the discussion is focused on answering the question of what life is from an engineering point of view. And, we approach the answer by examining the key steps of evolution from non-life to life. In this paper, five key steps of evolution from non-life to life will be discussed in detail. They are embodiment of energy flow, embodiment of signal flow, embodiment of knowledge flow, embodiment of decision flow, and embodiment of awareness flow. These findings are grounded on our engineering works toward the development of low-cost humanoid (LOCH) robot, and offer a unique perspective and an engineering basis. Whenever possible, the discussions in this paper are supported by real results of experiments on real robots.

Guilty robots, happy dogs: the question of alien minds

When we interact with animals, we intuitively read thoughts and feelings into their expressions and actions. It is easy--often irresistible--to suppose that they have minds like ours. And as technology grows more sophisticated, we might soon find ourselves interpreting the behavior even of robots in human terms. But is our natural tendency to humanize other beings philosophically or scientifically justifiable? Can we ever know what non-human minds are really like? How different are human minds from the minds of animals or robots? In Guilty Robots and Happy Dogs, David McFarland offers an accessible exploration of these and many other intriguing questions, questions that illuminate our understanding the human mind and its limits in knowing and imagining other minds. In exploring these issues, McFarland looks not only at philosophy, but also examines new evidence from the science of animal behavior, plus the latest developments in robotics and artificial intelligence, to show how many different--and often quite surprising--conclusions we can draw about the nature of minds alien to our own. Can robots ever feel guilty? Can dogs feel happy? Answering these questions is not simply an abstract exercise but has real implications for such increasingly relevant topics as animal welfare, artificial intelligence, and cybernetics. Engagingly and accessibly written, and thought-provoking from start to finish, Guilty Robots and Happy Dogs touches on the very nature of mind and its evolution. It is essential reading for anyone curious about animal behavior, robotics, artificial intelligence, or the nature of the human mind.

Expressing Robot's Mind by Human-like Motion

In this paper, we focus on a problem in designing for home robots. There are some design methods for conventional artifacts such as home electric appliances. However, not all methods can be applied effectively for robots. Especially, we deal with the situation in which a robot asks a user to help it by expressing its internal state. We then propose a novel design method ``motion overlapping (MO)'' by which a robot can perform human-like behavior to express its internal state. We consider that human-like behavior of a robot causes a user to understand its internal state ``mind'' easily. A small sweeping robot which performs ``back-and-forth'' motion is designed based on MO. In experiments, we compare the expressing by MO with sounding by a buzzer and lighting by a LED as conventional nonverbal expressing. We investigate effects on users' action of cooperating with the sweeping robot. We find that the expressing by MO causes most of the users to help the robot. The differences in those expressing methods are statistically significant. The results show that our proposed method is effective as one of design method for home robots.

ロボットの心の作り方―受動意識仮説に基づく基本概念の提案―

A fundamental idea for constructing a conscious robot is presented. First, hypotheses of the human mind are presented. The following ideas are shown: (1) The unconscious system is a recurrent network system made of various distributed subsystems. (2) Information of the mind such as intellect, feelings and willpower, is presumably processed in the unconscious system instead of the conscious system. (3) The conscious system just monitors, experiences afterward, models and memorizes the results of the unconscious system. (4) Realistic experiences of quality that the conscious system feels by itself are just illusions that are defined in the brain. Then an algorithm of a robot mind is constructed based on the hypotheses mentioned above. It is shown that a conscious mind of robots can be made using the proposed algorithm. Finally, purposes and issues of the robots with a mind are also discussed.

Mechanistic versus phenomenal embodiment: Can robot embodiment lead to strong AI?

Embodiment has become the raison d’etre for much of the new ‘cognitive robotics’. It fills a gap in the non-interactivist approach of traditional artificial intelligence (AI) in which ‘intelligence’ is viewed as the manipulation of symbols in a vacuum. However, a foundational question for the new AI is, can embodiment lead to a strong AI, i.e. a robot mind? To address this question, two extreme poles of embodiment are distinguished here, mechanistic and phenomenal. A detailed exploration of each type of embodiment is provided together with an appraisal of whether strong embodiment is possible for robotics, or whether robotics merely provides a tool for scientific exploration and modelling, i.e. weak embodiment? It is argued that strong embodiment, either mechanistic or phenomenal, is not possible for present day robots. However, weak embodiment may provide an enlightened approach to using robots for modelling cognition.

Reading a robot's mind: a model of utterance understanding based on the theory of mind mechanism

The purpose of this paper is to construct a methodology for smooth communication between humans and robots. Here, focus is on a mindreading mechanism, which is indispensable in human-human communication. We propose a model of utterance understanding based on this mechanism. Concretely speaking, we apply the model of a mindreading system to a model of human-robot communication. Moreover, we implement a robot interface system that applies our proposed model. The characteristic of our interface system is its ability to construct a relationship between a human and a robot by a method of having an agent, which interacts with the person, migrate from the mobile PC of the person to the robot. Psychological experiments were carried out to explore the validity of the following hypothesis: By reading a robot's mind based on such a relationship, a person can estimate the robot's intention with ease and, moreover, the person can even understand the robot's unclear utterances made by synthesized speech sounds. The results of the experiments statistically supported our hypothesis.

The Minds of Robots.

The Minds of Robots.