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This paper focuses on the opportunities and challenges associated with the use of artificial intelligence (AI) in academic library operations. In the quest to render fast, effective and efficient services, academic libraries have adopted different technologies in the past. Artificial intelligence technologies is the latest among the technologies currently being introduced in libraries. The technology which is considered an intelligent system, come in the form of robots and expert systems which have natural language processing, machine learning and pattern recognition capabilities. This paper examined the features of AI, the application of AI to library operations, examples of academic libraries with AI technologies in Sub-Saharan Africa, the need for AI in libraries and the challenges associated with the adoption of AI in libraries. The study concluded that AI holds a lot of prospects for the improvement of information services delivery in African academic libraries. Consequently, its adoption is a sinequanon to delivering robust library services in the Fourth Industrial Revolution (4IR). [ABSTRACT FROM AUTHOR]

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]

Taking into account the profound impact of technology on modern education, especially during the covid19 pandemic, increasing academic interest has focused towards the design and application of such tools on different learning contexts. A specific area of Human–Computer Interaction, called affordance theory, focuses on the perception, design and use of different technologies by educators and learners in learning contexts. This paper explores the impact of affordances in the process of creative problem in the context of playful educational robotics, with an intension of informing the design of future educational experiences around the field. The study capitalizes upon previous affordance propositions and frameworks in order to establish an affordance-based framework in the scope of playful educational robotics contexts, through the adoption of a qualitative research methodology, which was considered more appropriate as an exploratory tool. As part of the qualitative analysis, the study is mapping different types of affordances, related to such technologies, as well as an iterative creative problem-solving process that stems from learners' interactions with robotic artifacts, like the CreaCube playful robotics activity, which is presented in this study. [ABSTRACT FROM AUTHOR]

This is a conceptual paper that examines the origin and development of universities and their current role in global society. There has been an unprecedented and exponential growth of technology and artificial intelligence capabilities over the past ten years which is challenging current working practices and affecting all areas of society. The paper examines how this role may change to match the new demands placed on them by a digitally enabled society that has greater leisure time. The design of the paper is first to detail some of the changes in work practices that are taking place and how these will impact on society. It then offers several ways in which universities could modify their role to respond to these emerging challenges. This could include new courses, new organisational structures and new pedagogical practices. The paper provides a platform for discussion and debate around the strategic vision and direction of travel for higher education. [ABSTRACT FROM AUTHOR]

The Vision Guided Robotic systems (VGR) is an essential aspect of modern intelligent robotics. The VGR is rapidly transforming manufacturing processes by enabling robots to be highly adaptable and intelligent reducing the cost and com- plexity. For any sensor-based intelligent robots, vision-based planning is considered as one of the most prominent steps followed by controlled actions using visual feedback. To develop robust vision-based autonomous systems in robotic applica- tions, path-planning and localization can be implemented along with Visual Servoing (VS) for robust feedback control. In the available literature, most of the reviews are focused on a particular module of autonomous systems like path planning, motion planning strategies, or Visual Servoing techniques. In this paper overall review of different modules in vision-guided robotic systems is presented. So, this review provides researchers with broader in-depth knowledge about different modules that exist in the vision-guided autonomous system. The review also includes different vision sensors that are commonly used in industries covering their characteristics and applications. In this work, overall, 227 research papers in path planning and vision-based control algorithms are reviewed with recent intelligent techniques based on optimization and learning-based approaches. The graphical analysis illustrating the advancements of research in the field of vision-based robotics using Artificial Intelligence (AI) is also discussed. Lastly, this paper con- cludes by discussing some of the research gaps, challenges, and future directions existing in vision-based planning and control. [ABSTRACT FROM AUTHOR]

The convergence of large datasets, increased computational power, and enhanced algorithm design has led to the increased success of machine learning (ML) and artificial intelligence (AI) across a wide variety of healthcare professions but which, so far, have eluded formal discussion in physiotherapy. This is a concern as we begin to see accelerating performance improvements in AI research in general, and specifically, an increase in competence within narrow domains of practice in clinical AI. In this paper we argue that the introduction of AI-based systems within the health sector is likely to have a significant influence on physiotherapy practice, leading to the automation of tasks that we might consider to be core to the discipline. We present examples of some of these AI-based systems in clinical practice, specifically video analysis, natural language processing (NLP), robotics, personalized healthcare, expert systems, and prediction algorithms. We address some of the key ethical implications of these emerging technologies, discuss the implications for physiotherapists, and explore how the resultant changes may challenge some long-held assumptions about the status of the profession in society. [ABSTRACT FROM AUTHOR]

Qualitative spatial reasoning has been a core research topic in GIScience and AI for decades. It has been adopted in a wide range of applications such as wayfinding, question answering, and robotics. Most developed spatial inference engines use symbolic representation and reasoning, which focuses on small and densely connected data sets, and struggles to deal with noise and vagueness. However, with more sensors becoming available, reasoning over spatial relations on large-scale and noisy geospatial data sets requires more robust alternatives. This paper, therefore, proposes a subsymbolic approach using neural networks to facilitate qualitative spatial reasoning. More specifically, we focus on higher-order spatial relations as those have been largely ignored due to the binary nature of the underlying representations, e.g. knowledge graphs. We specifically explore the use of neural networks to reason over ternary projective relations such as between. We consider multiple types of spatial constraint, including higher-order relatedness and the conceptual neighborhood of ternary projective relations to make the proposed model spatially explicit. We introduce evaluating results demonstrating that the proposed spatially explicit method substantially outperforms the existing baseline by about 20%. [ABSTRACT FROM AUTHOR]

From artificial intelligence, predictive analytics and biometric sensors, to advanced robotics, virtual reality and mobile applications, rapid advances in technology are transforming the design, delivery and experience of mental healthcare. This paper explores the promise and pitfalls of three technologies (apps, avatars and robots) posed to transform mental health education, practice and research. By highlighting potential practical and ethical implications, this paper argues mental health professionals must actively engage in the co-design and development of new technologies, and reflexively reflect on whether the future will be a techno-utopia or dystopia. [ABSTRACT FROM AUTHOR]

RPA is used to complete repetitive tasks usually completed by humans. A robot is a software programed to mimic the operations of humans on computer systems. Robots are configured to accomplish these tasks using algorithms from the machine learning and artificial intelligence field. Using RPA leads to reduced errors in the processing of tasks and reduced completions times to carry out the tasks. This paper calculates the number of software licenses required to carry out thousands of transactions for financial services and proposes an assignment of the transactions to the robots. A two-phase linear integer optimization process is proposed. The first phase calculates the number of robots required to complete all of the tasks and the second phase assigns the tasks to the robots. Many different transaction types for financial services are to be completed, and each type has precise market operating hours, maximal delays in their treatment, different processing times and different volumes. The objective function minimizes the total number of robots, but also limits the number of robot startups and reconfigurations, since these operations take time to complete. The key behind the fast computing times to solve the problem is the formulation, where periods are defined at intersections of market operating hours. Preliminary tests are conducted on a real case provided by a bank in North America and a synthetic test case and show that the problem is solved quickly. [ABSTRACT FROM AUTHOR]

The EPSRC's Principles of Robotics advises the implementation of transparency in robotic systems, however research related to AI transparency is in its infancy. This paper introduces the reader of the importance of having transparent inspection of intelligent agents and provides guidance for good practice when developing such agents. By considering and expanding upon other prominent definitions found in literature, we provide a robust definition of transparency as a mechanism to expose the decision-making of a robot. The paper continues by addressing potential design decisions developers need to consider when designing and developing transparent systems. Finally, we describe our new interactive intelligence editor, designed to visualise, develop and debug real-time intelligence. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) systems are already playing an increasing role in many areas of professional activity, including the law, accountancy, building design and communications. This paper considers the types of activity which are likely to be largely replaced by AI in the near future and what the impact of AI could be on the conservation profession. It concludes that because of the hybrid nature of their work, conservators are probably less likely to be affected than many other professions, although the mix of tasks they undertake could be changed considerably in the near future. In particular, this paper argues that there may be less management-related activity in the future work of conservators, and more interventive, object-based work. [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]

While over the last few decades AI research has largely focused on building tools and applications, recent technological developments have prompted a resurgence of interest in building a genuinely intelligent artificial agent – one that has a mind in the same sense that humans and animals do. In this paper, I offer a theoretical and methodological framework for this project of investigating "artificial minded intelligence" (AMI) that can help to unify existing approaches and provide new avenues for research. I first outline three desiderata that a framework for AMI research should satisfy. In Section 1, I further motivate the desiderata as well as the need for a new framework. Section 2 develops a general methodological approach, the "generative methodology," and Section 3 develops a version of this methodology, the "Competence Framework for AI research" (CFAI). [ABSTRACT FROM AUTHOR]

This paper aims to use a visual-based control mechanism to control a quadrotor, which is in pursuit of a target. The non-linear nature of a quadrotor, on one hand, and the difficulty of obtaining an exact model for it, on the other hand, constitute two serious challenges in designing a controller for this UAV. A potential solution for such problems is the use of intelligent control methods such as those that rely on ANNs. A novel technique based on ANNs is proposed in this work for the identification and tracking of targets. Multilayer perceptron is used for this purpose. Experimental results and simulations are shown to demonstrate the feasibility of the proposedmethod for target tracking. [ABSTRACT FROM AUTHOR]

This paper presents a brief review of affordance research in robotics, with special concentrations on its applications in grasping and manipulation of objects. The concept of affordance could be a key to realize human-like advanced manipulation intelligence. First, we discuss the concept of affordance while associating with the applications in robotics. Then, we intensively explore the studies that utilize affordance for robotic manipulation applications, such as object recognition, grasping, and object manipulation including tool-use. They obtain and use affordance by several ways like learning from human, using simulation, and real-world execution. Moreover, we show our current work, which is a cloud database for advanced manipulation intelligence. The database accumulates various data related to manipulation task execution and will be an open platform to leverage various affordance techniques. [ABSTRACT FROM PUBLISHER]

This paper studies the problem of forming and updating the network topology in a multirobot system that is simultaneously engaged in a given task. The contribution of this paper is to propose a decentralized model of how the network may evolve based on network-related payoff functions and pairwise games. As such, pairwise games provide a practical and general scheme for contacting other robots and revising the network topology. A network is deemed acceptable by all the robots using pairwise stability and pairwise Nash equilibrium. As an application, we consider networks that are generated with mutual link-based payoff functions and show that – under some assumptions regarding changes in the configuration states – each game is ensured of converging to a pairwise stable network. This approach is then integrated with a common robotic task where the network is critical to successful task completion. The resulting performance is evaluated with respect to a variety of measures including task completion, network density, and the average payoff along with comparative results with all-to-all communication. [ABSTRACT FROM AUTHOR]

Advances in deep learning over the last decade have led to a flurry of research in the application of deep artificial neural networks to robotic systems, with at least 30 papers published on the subject between 2014 and the present. This review discusses the applications, benefits, and limitations of deep learning vis-à-vis physical robotic systems, using contemporary research as exemplars. It is intended to communicate recent advances to the wider robotics community and inspire additional interest in and application of deep learning in robotics. [ABSTRACT FROM AUTHOR]

This paper addresses autonomous intelligent navigation of mobile robotic platforms based on the recently reported algorithms of language-measure-theoretic optimal control. Real-time sensor data and model-based information on the robot's motion dynamics are fused to construct a probabilistic finite state automaton model that dynamically computes a time-dependent discrete-event supervisory control policy. The paper also addresses detection and avoidance of livelocks that might occur during execution of the robot navigation algorithm. Performance and robustness of autonomous intelligent navigation under the proposed algorithm have been experimentally validated on Segway RMP robotic platforms in a laboratory environment. [ABSTRACT FROM AUTHOR]

Future space projects will require space AI and robotics technology to construct, repair, and maintain satellites and space structures in orbit. The first paper is a survey paper on recent research and development activities on space robotics and AI, which introduces updated space programs and advanced technologies for future missions. Space robotics including Al is a key technology for planetary exploration as well as orbit service. [Extracted from the article]

Advanced autonomous artificial systems will need incremental learning and adaptive abilities similar to those seen in humans. Knowledge from biology, psychology and neuroscience is now inspiring new approaches for systems that have sensory-motor capabilities and operate in complex environments. Eye/hand coordination is an important cross-modal cognitive function, and is also typical of many of the other coordinations that must be involved in the control and operation of embodied intelligent systems. This paper examines a biologically inspired approach for incrementally constructing compact mapping networks for eye/hand coordination. We present a simplified node-decoupled extended Kalman filter for radial basis function networks, and compare this with other learning algorithms. An experimental system consisting of a robot arm and a pan-and-tilt head with a colour camera is used to produce results and test the algorithms in this paper. We also present three approaches for adapting to structural changes during eye/hand coordination tasks, and the robustness of the algorithms under noise are investigated. The learning and adaptation approaches in this paper have similarities with current ideas about neural growth in the brains of humans and animals during tool-use, and infants during early cognitive development. [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]

In this paper, we focus on the problem of having a multitude of very simple mobile robots self-organize their relative positions so as to obtain a variety of spatial configurations. The problem has a variety of applications in mobile robotics, modular robots, sensor networks, and computational self-assembly. The approach we investigate in this paper attempts at minimizing the local capability of robots and at verifying how and to what extent a variety of global shapes can be obtained by exploiting simple self-organizing algorithms and emergent behaviors. Several experiments are reported showing the effectiveness of the approach. [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]

In this paper we focus on how instructions for actions can be modelled in a self-organizing memory. Our approach draws from the concepts of regional distributed modularity and self-organization. We describe a self-organizing model that clusters action representations into different locations dependent on the body part they are related to. In the first case study we consider semantic representations of action verb meaning and then extend this concept significantly in a second case study by using actual sensor readings from our MIRA robot. Furthermore, we outline a modular model for a self-organizing robot action control system using language for instruction. Our approach for robot control using language incorporates some evidence related to the architectural and processing characteristics of the brain (Wermter et al. 2001b). This paper focuses on the neurocognitive clustering of actions and regional modularity for language areas in the brain. In particular, we describe a self-organizing network that realizes action clustering (Pulvermüller 2003). [ABSTRACT FROM AUTHOR]

This paper presents a noncertainty equivalent adaptive motion control scheme for robot manipulators in the absence of link velocity measurements. A new output feedback adaptation algorithm, based on the attractive manifold design approach, is developed. A proportional-integral adaptation is selected for the adaptive parameter estimator to strengthen the passivity of the system. In order to relieve velocity measurements, an observer is designed to estimate the velocities. The controller guarantees semiglobal asymptotic motion tracking and velocity estimation, as well as L?and L2bounded parameter estimation error. The effectiveness of the proposed controller is verified by simulations for a two-link robot manipulator and a four-bar linkage. The results are further compared with the earlier certainty-equivalent adaptive partial and full state feedback controller to highlight potential closed-loop performance improvements. [ABSTRACT FROM AUTHOR]

This paper focuses on adaptive motor control in the kinematic domain. Several motor-learning strategies from the literature are adopted to kinematic problems: ‘feedback-error learning’, ‘distal supervised learning’, and ‘direct inverse modelling’ (DIM). One of these learning strategies, DIM, is significantly enhanced by combining it with abstract recurrent neural networks. Moreover, a newly developed learning strategy (‘learning by averaging’) is presented in detail. The performance of these learning strategies is compared with different learning tasks on two simulated robot setups (a robot-camera-head and a planar arm). The results indicate a general superiority of DIM if combined with abstract recurrent neural networks. Learning by averaging shows consistent success if the motor task is constrained by special requirements. [ABSTRACT FROM AUTHOR]

This paper reports the first results of an innovative approach to modelling music cognition based on the emergent behaviour of interacting autonomous systems. A group of interactive autonomous singing robots were programmed to develop a shared repertoire of songs from scratch, after a period of spontaneous creations, adjustments and memory reinforcements. The robots interact with each other by means of vocal-like sounds. They use real sounds as opposed to software simulation. They are furnished with a physical model of the vocal tract, which synthesises vocal singing-like intonations, and a listening mechanism, which extracts pitch sequences from audio signals. The robots learn to imitate each other by babbling heard intonation patterns in order to evolve vectors of motor control parameters to synthesise the imitations. Models of the basic mechanisms underlying the emergence of songs are of great interest for musicians looking for hitherto unexplored ways to create music with interactive machines. [ABSTRACT FROM AUTHOR]

In recent years, advances and improvements in engineering and robotics have in part been due to strengthened interactions with the biological sciences. Robots that mimic the complexity and adaptability of biological systems have become a central goal in research and development in robotics. Usually, such a collaboration is addressed to a 2-fold perspective of (i) setting up anthropomorphic platforms as test beds for studies in neuroscience and (ii) promoting new mechatronic and robotic technologies for the development of bio-inspired or humanoid high-performance robotic platforms. This paper provides a brief overview of recent studies on sensorimotor coordination in human motor control and proposes a novel paradigm of adaptive learning for sensorimotor control, based on a multi-network high-level control architecture. The proposed neurobiologically inspired model has been applied to a robotic platform, purposely designed to provide anthropomorphic solutions to neuroscientific requirements. The goal of this work is to use the bio-inspired robotic platform as a test bed for validating the proposed model of high-level sensorimotor control, with the aim of demonstrating adaptive and modular control based on acquired competences, with a higher degree of flexibility and generality than conventional robotic controllers, while preserving their robustness. To this purpose, a set of object-dependent, visually guided reach-and-grasp tasks and the associated training phases were first implemented in a multi-network control architecture in simulation. Subsequently, the offline learning realized in simulation was used to produce the input command of reach-and-grasp to the low-level position control of the robotic platform. Experimental trials demonstrated that the adaptive and modular high-level control allowed reaching and grasping of objects located at different positions and objects of variable size, shape and orientation. A future goal would be to address autonomous and progressive learning based on growing competences. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the different approaches taken to date within the computer vision, robotics and artificial intelligence communities for the representation, recognition, synthesis and understanding of action. We deal with action at different levels of complexity and provide the reader with the necessary related literature references. We put the literature references further into context and outline a possible interpretation of action by taking into account the different aspects of action recognition, action synthesis and task-level planning. [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 results of an experiment in which a collection of simulated robots that have been evolved for the ability to solve a collective navigation problem develop a communication system that allows them to co-operate better. The analysis of the results obtained indicates how evolving robots develop a non-trivial communication system and exploit different communication modalities. The results also indicate how the possibility of co-adapting the robots' individual and social/communicative behaviour plays a key role in the development of progressively more complex and effective individuals. [ABSTRACT FROM AUTHOR]

Cultural heritage management is an inherently retrospective discipline. To the detriment of future heritage management, some heritage places were not recognised and managed even though they had instant global significance after their creation (e.g. sites of the Apollo space programme). The current revolution in robotic technologies, coupled with the developments in artificial intelligence, suggests that the creation of self‐reflective robots capable of semi‐independent thought (processes) is not too far away. This paper explores the conceptual and ethical issues that heritage managers face when dealing with the heritage such robots will create. [ABSTRACT FROM AUTHOR]

During the past two decades, embodiment has become an important concept in many areas of cognitive science, but so far there is no common understanding of what constitutes embodied cognition and what kind of body an artificial humanlike cognizer would require. Work in embodied artificial intelligence and robotics has addressed, to some degree, what kind of bodily implementation is necessary for embodied cognition, but crucial factors such as the role of social interaction and the ‘body-in-motion’ have still not received much attention. We argue that, in the human child, the interplay of social scaffolding and self-induced locomotion is fundamental to the development of joint attention and a ‘self’. Furthermore, we discuss the implications of the social dynamics of bodily experience for android science. We argue that keeping scientific and engineering perspectives apart, but also understanding their relation, is important for clarifying the objectives of android science.Parts of this paper have appeared in similar form in Lindblom and Ziemke (2005a, b) and Ziemke and Lindblom (2006). [ABSTRACT FROM AUTHOR]

In robot building, attention tends to focus on internal signal processing and the way desired motor signals are generated. But equally important is the selection and configuration of the robot's sensory resources. The degree to which sensory input informs the process of output generation directly impacts internal complexity since the more informative input signals are, the less internal processing is required. In the extreme case, input signals may identify motor signals exactly and internal processing may then be eliminated completely. The paper presents an information theoretic model for measuring this tradeoff being input information and internal processing complexity, and establishes the conditions under which it is feasible to utilize the processing-free strategy of ‘direct connection’. The model is then used for purposes of analysing five robots, including two commercial products (Sony AIBO and WowWee's Robosapiens). The principled exploitation of very informative sensory input is then shown to be effective in an unexpectedly wide range of situations. [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]

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]

This paper presents the design and implementation of an autonomous robot navigation system for intelligent target collection in dynamic environments. A feature-based multi-stage fuzzy logic (MSFL) sensor fusion system is developed for target recognition, which is capable of mapping noisy sensor inputs into reliable decisions. The robot exploration and path planning are based on a grid map oriented reinforcement path learning system (GMRPL), which allows for long-term predictions and path adaptation via dynamic interactions with physical environments. In our implementation, the MSFL and GMRPL are integrated into subsumption architecture for intelligent target-collecting applications. The subsumption architecture is a layered reactive agent structure that enables the robot to implement higher-layer functions including path learning and target recognition regardless of lower-layer functions such as obstacle detection and avoidance. The real-world application using a Khepera robot shows the robustness and flexibility of the developed system in dealing with robotic behaviors such as target collecting in the ever-changing physical environment. [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]

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]

Artificial evolution has been applied in mobile robotics to achieve adaptive robotic behaviors in unstructured environments. Meanwhile, much attention has been paid to the evolvable hardware, which is a novel set of integrated circuits capable of reconfiguring its architectures using evolutionary computing techniques. This paper presents the design and implementation of an evolvable hardware-based autonomous robot navigation system using intrinsic evolution. Distinguished from traditional evolutionary approaches based on software simulation, an evolvable robot controller at the hardware gate-level that is capable of adapting lo dynamic changes in the environments is implemented. In our approach, the concept of Boolean function is used to construct the evolvable controller implemented on an FPGA-based robot turret, and evolutionary computing is applied as a learning tool to guide the artificial evolution at the hardware level. The effectiveness of the proposed evolvable robotic system is confirmed with physical implementation of robot navigation behaviors on light source following and obstacle avoidance using a robot with traction fault. [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]