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The article presents a call for papers on artificial intelligence and robots for the library and information professions.

Domestic service robots (DSRs) that support people in everyday environments have been widely investigated. However, their ability to predict and describe future risks resulting from their own actions remains insufficient. In this study, we focus on the linguistic explainability of DSRs. Most existing methods do not explicitly model the region of possible collisions; thus, they do not properly generate descriptions of these regions. In this paper, we propose the Nearest Neighbor Future Captioning Model that introduces the Nearest Neighbor Language Model for future captioning of possible collisions, which enhances the model output with a nearest neighbors retrieval mechanism. Furthermore, we introduce the Collision Attention Module that attends regions of possible collisions, which enables our model to generate descriptions that adequately reflect the objects associated with possible collisions. To validate our method, we constructed a new dataset containing samples of collisions that can occur when a DSR places an object in a simulation environment. The experimental results demonstrated that our method outperformed baseline methods, based on the standard metrics. In particular, on CIDEr-D, the baseline method obtained 25.09 points, whereas our method obtained 33.08 points. [ABSTRACT FROM AUTHOR]

Creating autonomous robots that can actively explore the environment, acquire knowledge and learn skills continuously is the ultimate achievement envisioned in cognitive and developmental robotics. Importantly, if the aim is to create robots that can continuously develop through interactions with their environment, their learning processes should be based on interactions with their physical and social world in the manner of human learning and cognitive development. Based on this context, in this paper, we focus on the two concepts of world models and predictive coding. Recently, world models have attracted renewed attention as a topic of considerable interest in artificial intelligence. Cognitive systems learn world models to better predict future sensory observations and optimize their policies, i.e. controllers. Alternatively, in neuroscience, predictive coding proposes that the brain continuously predicts its inputs and adapts to model its own dynamics and control behavior in its environment. Both ideas may be considered as underpinning the cognitive development of robots and humans capable of continual or lifelong learning. Although many studies have been conducted on predictive coding in cognitive robotics and neurorobotics, the relationship between world model-based approaches in AI and predictive coding in robotics has rarely been discussed. Therefore, in this paper, we clarify the definitions, relationships, and status of current research on these topics, as well as missing pieces of world models and predictive coding in conjunction with crucially related concepts such as the free-energy principle and active inference in the context of cognitive and developmental robotics. Furthermore, we outline the frontiers and challenges involved in world models and predictive coding toward the further integration of AI and robotics, as well as the creation of robots with real cognitive and developmental capabilities in the future. [ABSTRACT FROM AUTHOR]

Technology in the hospitality industry is becoming increasingly important nowadays, because it has a close relationship with guest satisfaction and behavioral intention. This research aims to investigate hotel guests' preferences of in-room technology amenities by different demographic groups, and to find out how technology amenities including robots, artificial intelligence, and service automation, affect hotel guests' satisfaction, which in turn influence future behavioral intention to choose hotel and destination. This study uses a mixed quantitative and qualitative approach. Data are analyzed using SPSS and NVIVO. Results show that hotel guests' preferences of technology amenities vary significantly by their education levels and will influence guests' satisfaction and behavioral intention with regard to both hotel selection and destination selection. This paper appears to be the first attempt to investigate the influence of hotel guests' technology preferences on their behavioral intention in both the hotel and the tourism destination context. With the expectation to help the hotel industry, managerial implications and suggestions on technology adoption were provided on the basis of data analysis. [ABSTRACT FROM AUTHOR]

There is currently a great deal of confusion about the impact of robotisation, mainly due to the lack of clarity in research on timeframes and technologies. To overcome this, we distinguish between two basic narratives. The first - 'robots in the cages' - is an old story of automation which means that the industrial robots are pre-programmed high-precision machines that are used on a large scale but only in a few industries. In contrast, the emerging story - 'robots are moving out of the cages' - is about the more flexible and autonomous robots at present used on a small scale in the service sector, albeit the application areas are expanding rapidly.The objectives of this paper are first to analyse these two stories in terms of their technological, industrial and organisational characteristics, second, to examine the geographical pattern of global competition for robotisation in these narratives, and third, to discuss briefly the policy challenges involved. For the analysis, we use information from the International Federation of Robotics, the EPO Worldwide Patent Statistical Database (PATSTAT) and the Dealroom.co start-up database. [ABSTRACT FROM AUTHOR]

This paper shows that humans perceive the value of paintings made by AI as lower than the value of those made by humans when the creator of the work is known. This phenomenon occurs regardless of which style (figurative or abstract) the evaluated paintings have. The negative impact of the information that AI is the author of a painting is mediated by the author's artistry (AA) and the overall impression (OI) the painting makes on participants. Moreover, it was found that people use context cues when assessing paintings made by humans and AI. The perceived value of a painting increases when the information about the value of the painting made by AI was provided as a context cue, indicating that people exclude works created by AI from the category of human-made art. The method used in the research is an experiment made with 296 respondents (42.6% female). The age of the participants ranged between 18 and 62 (M = 35.30, SD = 10.76). [ABSTRACT FROM AUTHOR]

In this paper we analyze imaginaries about care robots using a set of interviews with roboticists. The study of imaginaries – from a notion close to that of Castoriadis's radical imaginary – is used as a tool to unravel ethical, political and social concerns that care robots entail. From the analysis of the interviews, our results highlight that imaginaries regarding care robots are predominantly sustained by a social process of care fragmentation. The translation of the imaginary of industry robots into the wildness of the daily life in healthcare reconfigures the comprehension of robots and their mediations. This process is intensively linked to Human Robot Collaboration (HRC) and Artificial Intelligence (AI) imaginaries of care, based on the cult of domesticity and the opposition of human caring to rational caring. We see how these fragmentations are in tension with an approach that seeks to integrate the ethics of care with technoscience, which has relevant consequences for the ethical debate on care robotics and the political significance of care in our world. [ABSTRACT FROM AUTHOR]

In 2017, the European Parliament issued a Resolution calling on the Commission to elaborate new solutions based on civil law that could respond to the rapid present-day development of robotics and AI. The Resolution, pushing for the preparation of new tort law focusing on robots, postulates that a new definition of robot be prepared. Responding to the Resolution, this paper consists in a legal-cognitive-linguistic analysis which draws three conclusions: firstly, that the definitional method is not the best approach to determining the scope of the regulation of robotics and AI; secondly, that the Resolution is incorrect in assuming that a new civil law solution should turn on differentiating between AI and robots and that robots should be treated as focal in determining the scope of the regulation; and, thirdly, that any new norms should be rooted in the concept of AI and not, as proposed by the Resolution, in the concept of robot. [ABSTRACT FROM AUTHOR]

Hotel industry started to adopt service robots, which are considered a future workforce. However, no attempt was conducted to examine the dimensionality of service quality of service robots. This paper aims to understand the influence of human–robot interaction from the viewpoint of hoteliers and guests. Two studies are conducted in this respect. Study 1 organizes focus-group interviews with hotel managers from various departments to elicit themes related to guest–robot interaction and robot-delivered services. Based on the findings in Study 1, Study 2 conducts an experiment to examine and compare hotel guests' perceptions about the quality of services provided by human staff and service robots, as well as their joint services. Human staff services are perceived higher than the services of service robots in terms of interaction quality and physical service environment. However, no significant difference in outcome quality is noted. [ABSTRACT FROM AUTHOR]

This report summarises the papers, presentations and discussion of the Artificial Intelligence and Simulation of Behaviour special workshop re-evaluating the Engineering and Physical Science and Arts and Humanities Research Councils (EPSRC and AHRC) 2010 Principles of Robotics. We describe the call for papers re-examining the workshop, summarise the papers and discussions that took place, and the voting that lead to our workshop adopting a series of proposals for amending the original Principles. The workshop discussed and voted on 14 specific “amendments, additions, or reflections” on the Principles. Of these, 9 out 14 were adopted by majority vote, 6 receiving strong support (67% or more in favour), 1 majority support (53%), with several of the remaining receiving mixed support of between 33% and 47%. An important and unanimous conclusion of the workshop was that “the Principles should be amended through a thorough and inclusive process”. Adopted proposals also highlighted the need to “focus on the protection of humanity” from possible future risks created by AI and robotics, and to take into account how society is changing and adapting to technological advances. [ABSTRACT FROM AUTHOR]

The thought-provoking EPSRC Principles of Robotics stem largely from the reflection on the extent to which robots can affect our lives. These comments highlight the fact that, while the principles may address to a good extent the present technological challenges, they appear to be less immediately suited for future technological and conceptual dares. The first part of the paper is dedicated to the search of the definition of what a robot is. Such a definition should offer the basic conceptual platform on which a normative endeavour, aiming to regulate robots in society, should be based. Concluding that the Principles offer no clear yet flexible insight into such a (meta-) definition, which would allow one to take into account the parameters of informed technological imagination and of envisaged social transformation, the second half of the paper highlights a number of regulatory points of tension. Such tensions, it is argued, stem largely from the absence of an appropriate conceptual platform, influencing negatively the extent to which the principles can be effective in guiding social, ethical, legal and scientific conduct. [ABSTRACT FROM PUBLISHER]

In the preceding paper, a method for mobile robot navigation control based on discrimination of multiple artificial fields was introduced. In this second paper, the method is extended to robot formations. Experimental demonstrations are presented taking examples of four types of formations. The experiments cover formation initialization, maneuvering, obstacle avoidance and formation switching. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the limitations of current evolutionary robotics models and we propose a new framework that might solve some of these problems and lead to art open-ended evolutionary process in hardware. More specifically, the paper describes a novel approach where the usual concepts of population, generations and fitness tire made implicit in the system. Individuals co-evolve embedded in their environment. Exploiting the sell-assembling capabilities of the (simulated) robots, the genotype of a successful individual can spread in the population. In this way, interesting behaviours emerge spontaneously, resulting in chasing and evading other individuals, collective obstacle avoidance and co-ordinated motion of self-assembled structures. [ABSTRACT FROM AUTHOR]

This paper proposes a method of acquisition of a page turning skill for a multifingered robotic hand using reinforcement learning. The goal of this paper is generation of the manipulation skill of a flexible object without its explicit model and tactile sensation during its control. In this paper, a page turning task is considered as an example of such tasks, where a sheet of paper, generally used in books, is a flexible object. The fingertip trajectories are obtained by reinforcement learning based on simulation. The reward considering a friction condition is given, so that a page turning skill without slip between the finger and the paper is obtained. The validity is confirmed by an experimental system which consists of a 2-d.o.f. manipulator and two 2-d.o.f. fingers. [ABSTRACT FROM AUTHOR]

Developmental robotics is an emerging field located at the intersection of robotics, cognitive science and developmental sciences. This paper elucidates the main reasons and key motivations behind the convergence of fields with seemingly disparate interests, and shows why developmental robotics might prove to be beneficial for all fields involved. The methodology advocated is synthetic and two-pronged: on the one hand, it employs robots to instantiate models originating from developmental sciences; on the other hand, it aims to develop better robotic systems by exploiting insights gained from studies on ontogenetic development. This paper gives a survey of the relevant research issues and points to some future research directions. [ABSTRACT FROM AUTHOR]

Taddeo and Floridi [2007. A praxical solution of the symbol grounding problem.Minds and Machines,17, 369–389. (This paper is reprinted in Floridi, L. (2011). The philosophy of information. Oxford: Oxford University Press)] propose a solution to the symbol grounding problem (SGP). Unfortunately, their proposal, while certainly innovative, interesting and – given the acute difficulty of SGP – brave, merely shows that a class of robots can in theory connect, in some sense, the symbols it manipulates with the external world it perceives, and can, on the strength of that connection, communicate in sub-human fashion. [ABSTRACT FROM PUBLISHER]

In this paper we propose a latent Dirichlet allocation (LDA)-based framework for multimodal categorization and words grounding by robots. The robot uses its physical embodiment to grasp and observe an object from various view points, as well as to listen to the sound during the observing period. This multimodal information is used for categorizing and forming multimodal concepts using multimodal LDA. At the same time, the words acquired during the observing period are connected to the related concepts, which are represented by the multimodal LDA. We also provide a relevance measure that encodes the degree of connection between words and modalities. The proposed algorithm is implemented on a robot platform and some experiments are carried out to evaluate the algorithm. We also demonstrate simple conversation between a user and the robot based on the learned model. [ABSTRACT FROM AUTHOR]

Humanoid robots have become appealing to the research community because of their potential versatility. However, traditional programming approaches may not reveal their full capabilities. Thus, an important goal is to develop a humanoid robot that can learn to perform complex tasks by itself. This paper proposes a method to recognize and regenerate motion in a humanoid robot. We demonstrate how a sequence of high-dimensional motion data can be automatically segmented into abstract action classes. The sequence from a 25-d.o.f. humanoid robot performing a ball tracking task is reduced to its intrinsic dimensionality by non-linear principal component analysis (NLPCA). The motion data is then segmented automatically by incrementally generating NLPCA networks with a circular constraint and assigning to these networks data points according to their temporal order in a conquer-and-divide fashion. Repeated motion patterns are removed based on their proximity to similar motion patterns in the reduced sensorimotor space to derive a nonredundant set of abstract actions. The networks abstracted five motion patterns without any prior information about the number or type of motion patterns. We ensured the motion reproduction by employing a motion optimization algorithm based on the learning of the sensorimotor mapping in the low-dimensional space. [ABSTRACT FROM AUTHOR]

The existing reinforcement learning methods have been seriously suffering from the curse of the dimension problem, especially when they are applied to multiagent dynamic environments. One of the typical examples is a case of RoboCup competitions since other agents and their behavior easily cause state and action space variations. This paper presents a method of modular learning in a multiagent environment by which the learning agent can acquire cooperative behavior with its teammates and competitive behavior against its opponents. The key ideas to resolve the issue are as follows. First, a two-layer hierarchical system with multilearning modules is adopted to reduce the size of the sensor and action spaces. The state space of the top layer consists of the state values from the lower level and the macro actions are used to reduce the size of the physical action space. Second, the state of the other, to what extent it is close to its own goal, is estimated by observation and used as a state variable in the top layer state space to realize the cooperative/competitive behavior. The method is applied to a four (defense team)-on-five (offense team) game task and the learning agent (a passer of the offense team) successfully acquired the teamwork plays (pass and shoot) within much shorter learning time. [ABSTRACT FROM AUTHOR]

One of the most distinguishing features of cognitive systems is the ability to predict the future course of actions and the results of ongoing behaviors, and in general to plan actions well in advance. Neuroscience has started examining the neural basis of these skills with behavioral or animal studies and it is now relatively well understood that the brain builds models of the physical world through learning. These models are sometimes called 'internal models', meaning that they are the internal rehearsal (or simulation) of the world enacted by the brain. In this paper we investigate the possibility of building internal models of human behaviors with a learning machine that has access to information in principle similar to that used by the brain when learning similar tasks. In particular, we concentrate on models of reaching and grasping, and we report on an experiment in which biometric data collected from human users during grasping was used to train a support vector machine. We then assess to what degree the models built by the machine are faithful representations of the actual human behaviors. The results indicate that the machine is able to predict reasonably well human reaching and grasping, and that prior knowledge of the object to be grasped improves the performance of the machine, while keeping the same computational cost. [ABSTRACT FROM AUTHOR]

Researchers in robotics and artificial intelligence have often looked at biology as a source of inspiration for solving their problems. From the opposite perspective, neuroscientists have recently turned their attention to the use of robotic systems as a way to quantitatively test and analyze theories that would otherwise remain at a speculative stage. Computational models of neurons and networks of neurons are often activated with simplified artificial patterns that bear little resemblance to natural stimuli. The use of robotic systems has the advantage of introducing phenotypic and environmental constraints similar to those that brains of animals have to face during development and in everyday life. Consideration of these constraints is particularly important in light of modern brain theories, which emphasize the importance of closing the perception/action loop between the agent and the environment. To provide concrete examples of the use of robotic systems in neuroscience, this paper reviews our work in the areas of sensory perception and motor learning. The interdisciplinary approach followed by this research establishes a direct link between natural sciences and engineering. This research can lead to the understanding of basic biological problems while producing robust and flexible systems that operate in the real world. [ABSTRACT FROM AUTHOR]

Simultaneous localization and map-building (SLAM) continues to draw considerable attention in the robotics community due to the advantages it can offer in building autonomous robots. It examines the ability of an autonomous robot starting in an unknown environment to incrementally build an environment map and simultaneously localize itself within this map. Recent advances in computer vision have contributed a whole class of solutions for the challenge of SLAM. This paper surveys contemporary progress in SLAM algorithms, especially those using computer vision as main sensing means, i.e., visual SLAM. We categorize and introduce these visual SLAM techniques with four main frameworks: Kalman filter (KF)-based, particle filter (PF)-based, expectation-maximization (EM)-based and set membership-based schemes. Important topics of SLAM involving different frameworks are also presented. This article complements other surveys in this field by being current as well as reviewing a large body of research in the area of vision-based SLAM, which has not been covered. It clearly identifies the inherent relationship between the state estimation via the KF versus PF and EM techniques, all of which are derivations of Bayes rule. In addition to the probabilistic methods in other surveys, non-probabilistic approaches are also covered. [ABSTRACT FROM AUTHOR]

In this paper we present the agent architecture development environment (ADE), intended for the design, implementation, and testing of distributed robotic agent architectures. ADE is unique among robotic architecture development environments in that it is based on a universal agent architecture framework called APOC, which allows it to implement architectures in any design methodology, and in that it uses an underlying multi-agent system to allow for the the distribution of architectural components over multiple host computers. After a short exposition of the theory behind ADE, we present the multi-agent system setup and give an example of using ADE in a multi-robot setting. A general discussion then highlights some of the novel features of ADE and illustrates how ADE can be used for designing, implementing, testing, and running agent architectures. [ABSTRACT FROM AUTHOR]

In this paper, we experimentally investigated the open-end interaction generated by the mutual adaptation between humans and robot. Its essential characteristic, incremental learning, is examined using the dynamical systems approach. Our research concentrated on the navigation system of a specially developed humanoid robot called Robovie and seven human subjects whose eyes were covered, making them dependent on the robot for directions. We used the usual feed-forward neural network (FFNN) without recursive connections and the recurrent neural network (RNN) for the robot control. Although the performances obtained with both the RNN and the FFNN improved in the early stages of learning, as the subject changed the operation by learning on its own, all performances gradually became unstable and failed. Next, we used a 'consolidation-learning algorithm' as a model of the hippocampus in the brain. In this method, the RNN was trained by both new data and the rehearsal outputs of the RNN not to damage the contents of current memory. The proposed method enabled the robot to improve performance even when learning continued for a long time (open-end). The dynamical systems analysis of RNNs supports these differences and also showed that the collaboration scheme was developed dynamically along with succeeding phase transitions. [ABSTRACT FROM AUTHOR]

A major facet of multi-legged robot control is locomotion. Each leg must move in such a manner that it efficiently produces thrust and provides maximum support. The motion of all the legs must be coordinated so that they are working together to provide constant stability while propelling the robot forward. In this paper, we discuss the use of a cyclic genetic algorithm (CGA) to evolve control programs that produce gaits for actual hexapod robots. Tests done in simulation and verified on the actual robot show that the CGA successfully produces gaits for both fully capable and disabled robots. [ABSTRACT FROM AUTHOR]

The paper discusses the concept of re-planning for a mobile robot in the presence of semidynamic obstacles. The navigational planning is done by employing genetic algorithm until it reaches the goal point. The path segments traversed by the mobile robot are stored by a simple matrix, employing temporal associative memory. During subsequent traversal, the robot utilizes the previously stored matrix to avoid an obstacle path. In case of deadlock, the robot back tracks using TAM and finds alternative paths to reach the goal. This algorithm has been realized on a Pioneer 2DX mobile robot of ActiveMedia Robotic LLC, USA, through client server architecture. The result shows that the robot reaches the goal within a vicinity of a 20 mm radius. [ABSTRACT FROM AUTHOR]

A remote controlled robot for collecting information in disasters, e.g. earthquakes, is one of most effective applications of robots, because it is very dangerous for human beings to locate survivors in collapsed buildings and, in addition, small robots can move into narrow spaces to find survivors. However, previous rescue systems that use robots have a significant problem — a shortage of operators. In catastrophic disasters, in order to save victims, we must explore wide areas within a limited time. Thus, many rescue robots should be employed simultaneously. However, human interfaces of previous rescue robots were complicated, so that well-trained professional operators were needed to operate the robots and, thus, to use many rescue robots, many professional operators were required. However, in such catastrophic disasters it is difficult to get many professional operators together within a short time. In this paper we address the problem and propose a concept of rescue team organization in which professional rescue staff and volunteer staff work together for handling a catastrophic disaster. We point out the necessity for rescue robots which can be operated easily by non-professional volunteer staff. To realize a rescue robot which can be operated easily, we propose a rescue robot system which has a human interface seen in typical, everyday vehicles and a snake-like robot which has mechanical intelligence. We have demonstrated the validity and the effectiveness of the proposed concept by developing a prototype system. [ABSTRACT FROM AUTHOR]

For the coordinated control of multifingered robot hands it is important to simultaneously control both the position of a target object and the force exerted on it. In particular, the 'internal force' which does not affect the motion of the object should be controlled in order not to break it. This paper proposes an extension of the 'kinetostatic filtering method' by using a pseudo-inverse of the Jacobian. In addition, a new control signal is introduced for posture control of the fingertips. Experiments with a two-fingered hand are conducted to verify the capacity of the proposed controller and to show the transient response of it. [ABSTRACT FROM AUTHOR]

Many researchers in embodied cognitive science and artificial intelligence, and evolutionary robotics in particular, emphasize the interaction or brain, body and environment as crucial to the emergence of intelligent. adaptive behaviour. Accordingly. the interaction between agent and environment. as well as the co-adaptation of artificial brains and bodies, has been the focus of much research in evolutionary robotics. Hence, there are plenty of studies of robotic agents/species adapting to a given environment. Many animals, on the other hand, in particular humans, to some extent can choose to adapt the environment to their own needs instead of adapting (onIy) themselves. That alternative has been studied relatively little in robot experiments. This paper, therefore, presents some simple initial simulation experiments, in a delayed response task setting, that illustrate how the evolution of environment adaptation can sent to provide cognitive scaffolding that reduces the requirements for individual agents. Furthermore, theoretical implications, open questions and future research directions for evolutionary robotics are discussed. [ABSTRACT FROM AUTHOR]

Microscopic analysis is a standard approach in the study of robot behaviour. Typically, the approach comprises the analysis of a single (or sometimes a few) robot-environment system(s) to reveal specific properties of robot behaviour. In contrast to microscopic analysis, macroscopic analysis focuses on averaged properties of systems. The advantage is that such a property is easier to generalize so that it can be established to what extent the property is universal. This paper investigates whether a macroscopic analysis can reveal a universal property of adaptive behaviour in a robot model of foraging behaviour. Our analysis reveals that the step lengths of the most successful robots are distributed according to a Lévy-flight distribution. From studies on a variety of natural species, it is known that such a distribution constitutes a universal property of foraging behaviour. Thereafter, we discuss an example of how macroscopic analysis can be applied to existing research in evolutionary robotics, and relate the macroscopic and microscopic analyses of foraging behaviour to the framework of scientific research described by Cohen (1995, Empirical Methods for Artificial Intelligence (Cambridge MA: MIT Press)). We conclude that macroscopic analysis may predict universal properties of adaptive behaviour and that it may complement microscopic analysis in the study of adaptive behaviour. [ABSTRACT FROM AUTHOR]

When developing teams of humanoids — rather than an individual humanoid — there are a number of issues that become important to consider, including robustness, scalability, versatility, and also development and production costs. Therefore, we used a modern approach to AI that puts emphasis on the balance between control, electronic hardware, material, sensory system and energy in order to develop the team of Viki humanoid robots. In contrast to the top-down approach of equipping a humanoid with as many sensors, motors, power, etc., as possible, we developed a bottom-up approach to the construction of humanoids, regarding both hardware and software (modular behaviorbased control). The approach is shown with the development of the Viki humanoid team that won the RoboCup Humanoids Free Style World Championship 2002. In this paper, we focus on the main result of the behavior-based architecture with many layers of behaviors at different levels, which make it easy for both engineers to design new behaviors and for end-users to develop humanoid behaviors at different levels of complexity, dependent on the competencies of the individual end-user. With this architecture, it becomes possible to develop simple user interfaces with a user-guided implementation of our modular behavior-based approach, in order to allow any user to design performances with the humanoid robots. [ABSTRACT FROM AUTHOR]

Considering that intelligent robotic systems work in a real environment, it is important that they themselves have the ability to determine their own internal conditions. Therefore, we consider it necessary to pay some attention to the diagnosis of such intelligent systems and to construct a system for the self-diagnosis of an autonomous mobile robot. Autonomous mobile systems must have a self-contained diagnostic system and therefore there are restrictions to building such a system on a mobile robot. In this paper, we describe an internal state sensory system and a method for diagnosing conditions in an autonomous mobile robot. The prototype of our internal sensory system consists of voltage sensors, current sensors and encoders. We show experimental results of the diagnosis using an omnidirectional mobile robot and the developed system. Also, we propose a method that is able to cope with the internal condition using internal sensory information. We focus on the functional units in a single robot system and also examine a method in which the faulty condition is categorized into three levels. The measures taken to cope with the faulty condition are set for each level to enable the robot to continue to execute the task. We show experimental results using an omnidirectional mobile robot with a self-diagnosis system and our proposed method. [ABSTRACT FROM AUTHOR]

One of the most important abilities for an agent's cognitive development in a social environment is the ability to recognize and imitate actions of others. In this paper we describe a cognitive architecture for action recognition and imitation, and present experiments demonstrating its implementation in robots. Inspired by neuroscientific and psychological data, and adopting a 'simulation theory of mind' approach, the architecture uses the motor systems of the imitator in a dual role, both for generating actions, and for understanding actions when performed by others. It consists of a distributed system of inverse and forward models that uses prediction accuracy as a means to classify demonstrated actions. The architecture is also shown to be capable of learning new composite actions from demonstration. [ABSTRACT FROM AUTHOR]

Studies on emotion are currently receiving a lot of attention. The importance of emotion in the development and support of intelligent and social behaviour has been highlighted by studies in psychology and neurology. Hence, the recognition of affective states has also become a critical feature in robot social development, with robots assumed to take on a role as social companion. In this paper, we address the issue of endowing robots with the ability to learn incrementally to recognize the affective state of their human partner by interpreting their gestural cues. We propose a model that can self-organize postural features into affective categories, and use contextual feedback from the partner to drive the learning process. [ABSTRACT FROM AUTHOR]

This paper presents a constructive model by which a robot acquires the ability of joint attention with a human caregiver based on its embedded mechanisms of visual attention and learning with self-evaluation. The former is to look at a salient object in the robot's view, and the latter is to learn sensorimotor co-ordination when visual attention has succeeded. Since the success of visual attention does not always correspond to the success of joint attention, the robot has incorrect learning data for joint attention as well as correct data. However, the robot is expected statistically to lose incorrect data as outliers since such data do not have any correlation in the sensorimotor co-ordination while correct data have a correlation. The robot consequently acquires the ability of joint attention by finding the correlation in the sensorimotor co-ordination even if multiple objects are placed at random positions in an environment and a human caregiver does not provide any task evaluation to the robot. The experimental results show that the proposed model makes the robot reproduce the developmental process of infants' joint attention. Therefore, the proposed model could be one of the models to explain how infants develop the ability of joint attention. [ABSTRACT FROM AUTHOR]

This paper provides a summary of new results coming from developmental infancy research demonstrating preverbal infants' early competence in understanding and learning from the intentional actions of other agents. The reviewed studies (using violation-of-expectation and observational learning paradigms) provide converging evidence that by the end of the first year infants can interpret and draw systematic inferences about other agents' goal-directed actions, and can rely on such inferences in observational learning when imitating others' actions or emulating their goals. To account for these findings it is proposed that young infants possess a non-mentalistic action interpretational system, the 'teleological stance' (Gergely and Csibra 2003) that represents actions by relating three relevant aspects of reality (action, goal-state, and situational constraints) through the inferential 'principle of rational action', which assumes that: (a) the basic function of actions is to bring about future goal-states; and that (b) agents will always perform the most efficient means action available to them within the constraints of the given situation. The relevance of these research findings and the proposed theoretical model for how to realize the future goal of epigenetic robotics of building a 'socially relevant' humanoid robot is then discussed. [ABSTRACT FROM AUTHOR]