The Dawning of the Ethics of Environmental Robots

Environmental scientists and engineers have been exploring research and monitoring applications of robotics, as well as exploring ways of integrating robotics into ecosystems to aid in responses to accelerating environmental, climatic, and biodiversity changes. These emerging applications of robots and other autonomous technologies present novel ethical and practical challenges. Yet, the critical applications of robots for environmental research, engineering, protection and remediation have received next to no attention in the ethics of robotics literature to date. This paper seeks to fill that void, and promote the study of environmental robotics. It provides key resources for further critical examination of the issues environmental robots present by explaining and differentiating the sorts of environmental robotics that exist to date and identifying unique conceptual, ethical, and practical issues they present.     

Model learning for robot control: a survey

Models are among the most essential tools in robotics, such as kinematics and dynamics models of the robot's own body and controllable external objects. It is widely believed that intelligent mammals also rely on internal models in order to generate their actions. However, while classical robotics relies on manually generated models that are based on human insights into physics, future autonomous, cognitive robots need to be able to automatically generate models that are based on information which is extracted from the data streams accessible to the robot. In this paper, we survey the progress in model learning with a strong focus on robot control on a kinematic as well as dynamical level. Here, a model describes essential information about the behavior of the environment and the influence of an agent on this environment. In the context of model-based learning control, we view the model from three different perspectives. First, we need to study the different possible model learning architectures for robotics. Second, we discuss what kind of problems these architecture and the domain of robotics imply for the applicable learning methods. From this discussion, we deduce future directions of real-time learning algorithms. Third, we show where these scenarios have been used successfully in several case studies.     

Learning data-driven decision-making policies in multi-agent environments for autonomous systems

Autonomous systems such as Connected Autonomous Vehicles (CAVs), assistive robots are set improve the way we live. Autonomous systems need to be equipped with capabilities to Reinforcement Learning (RL) is a type of machine learning where an agent learns by interacting with its environment through trial and error, which has gained significant interest from research community for its promise to efficiently learn decision making through abstraction of experiences. However, most of the control algorithms used today in current autonomous systems such as driverless vehicle prototypes or mobile robots are controlled through supervised learning methods or manually designed rule-based policies. Additionally, many emerging autonomous systems such as driverless cars, are set in a multi-agent environment, often with partial observability. Learning decision making policies in multi-agent environments is a challenging problem, because the environment is not stationary from the perspective of a learning agent, and hence the Markov properties assumed in single agent RL does not hold. This paper focuses on learning decision-making policies in multi-agent environments, both in cooperative settings with full observability and dynamic environments with partial observability. We present experiments in simple, yet effective, new multi-agent environments to simulate policy learning in scenarios that could be encountered by an autonomous navigating agent such as a CAV. The results illustrate how agents learn to cooperate in order to achieve their objectives successfully. Also, it was shown that in a partially observable setting, an agent was capable of learning to roam around its environment without colliding in the presence of obstacles and other moving agents. Finally, the paper discusses how data-driven multi-agent policy learning can be extended to real-world environments by augmenting the intelligence of autonomous vehicles.     

An architecture for ethical robots inspired by the simulation theory of cognition

The expanding ability of robots to take unsupervised decisions renders it imperative that mechanisms are in place to guarantee the safety of their behaviour. Moreover, intelligent autonomous robots should be more than safe; arguably they should also be explicitly ethical. In this paper, we put forward a method for implementing ethical behaviour in robots inspired by the simulation theory of cognition. In contrast to existing frameworks for robot ethics, our approach does not rely on the verification of logic statements. Rather, it utilises internal simulations which allow the robot to simulate actions and predict their consequences. Therefore, our method is a form of robotic imagery. To demonstrate the proposed architecture, we implement a version of this architecture on a humanoid NAO robot so that it behaves according to Asimov’s laws of robotics. In a series of four experiments, using a second NAO robot as a proxy for the human, we demonstrate that the Ethical Layer enables the robot to prevent the human from coming to harm in simple test scenarios.     

Computational modeling/cognitive robotics complements functional modeling/experimental psychology

This position paper explores the possible contributions to the science of psychology from insights obtained by building and experimenting with cognitive robots. First, the functional modeling characteristic of experimental psychology is discussed. Second, the computational modeling required for cognitive robotics is described, and possible experiments with them are illustrated. Next, we argue that cognitive developmental robots, robots that “live” through a development phase where they learn about their environments in several different modes, can provide additional benefits to the science of psychology. Finally, the reciprocal interactions between computational modeling/cognitive robotics and functional modeling/experimental psychology are explored. We conclude that each can contribute significantly to the other.     

The Application of Cognitive‐Developmental or Mediated Cognitive Learning Strategies in Online College Coursework

This research article explores the active use of cognitive‐developmental or mediated cognitive learning strategies in undergraduate online courses. Examples and applications are drawn from two online sessions integrating online interaction, essay and discussion assignments, as well as a variety of multimedia components conducted during the spring of 2008. While focus on the interaction among students remains an important aspect of the online discussion environment, particular attention is given to the interaction between the student and the instructor. This paper argues that while online learning environments are ultimately student‐controlled, they should be teacher‐centered. The findings of this research suggest that students are more directly influenced by an instructor's intentional effort to mediate the learning process than by the course objectives, material, or subject matter. Successful use of online technologies requires deliberate action on the part of the instructor to integrate various mediated cognitive learning strategies: (a) student participation and response is significantly increased, and (b) student motivation and morale is dramatically influenced.     

The Robot in the Crib: A Developmental Analysis of Imitation Skills in Infants and Robots

Interesting systems, whether biological or artificial, develop. Starting from some initial conditions, they respond to environmental changes, and continuously improve their capabilities. Developmental psychologists have dedicated significant effort to studying the developmental progression of infant imitation skills, because imitation underlies the infant's ability to understand and learn from his or her social environment. In a converging intellectual endeavour, roboticists have been equipping robots with the ability to observe and imitate human actions because such abilities can lead to rapid teaching of robots to perform tasks. We provide here a comparative analysis between studies of infants imitating and learning from human demonstrators, and computational experiments aimed at equipping a robot with such abilities. We will compare the research across the following two dimensions: (a) initial conditions-what is innate in infants, and what functionality is initially given to robots, and (b) developmental mechanisms-how does the performance of infants improve over time, and what mechanisms are given to robots to achieve equivalent behaviour. Both developmental science and robotics are critically concerned with: (a) how their systems can and do go 'beyond the stimulus' given during the demonstration, and (b) how the internal models used in this process are acquired during the lifetime of the system.     

Synthesis of autonomous robots through evolution

Evolutionary robotics is the attempt to develop robots through a self-organized process based on artificial evolution. This approach stresses the importance of the study of systems that have a body and that are situated in a physical environment, and which autonomously develop their own skills in close interaction with the environment. In this review we briefly illustrate the method and the main concept of evolutionary robotics, and examine the most significant contribution in this area. We also discuss some of the contributions that this research area is making to the foundational debate in cognitive science.     

The basis of shared intentions in human and robot cognition

There is a fundamental difference between robots that are equipped with sensory, motor and cognitive capabilities, vs. simulations or non-embodied cognitive systems. Via their perceptual and motor capabilities, these robotic systems can interact with humans in an increasingly more “natural” way, physically interacting with shared objects in cooperative action settings. Indeed, such cognitive robotic systems provide a unique opportunity to developmental psychologists for implementing their theories and testing their hypotheses on systems that are becoming increasingly “at home” in the sensory--motor and social worlds, where such hypotheses are relevant. The current research is the result of interaction between research in computational neuroscience and robotics on the one hand, and developmental psychology on the other. One of the key findings in the developmental psychology context is that with respect to other primates, humans appear to have a unique ability and motivation to share goals and intentions with others. This ability is expressed in cooperative behavior very early in life, and appears to be the basis for subsequent development of social cognition. Here we attempt to identify a set of core functional elements of cooperative behavior and the corresponding shared intentional representations. We then begin to specify how these capabilities can be implemented in a robotic system, the Cooperator, and tested in human–robot interaction experiments. Based on the results of these experiments we discuss the mutual benefit for both fields of the interaction between robotics and developmental psychology.     

Behavior-based robotics as a tool for synthesis of artificial behavior and analysis of natural behavior

Work in behavior-based systems focuses on functional modeling, that is, the synthesis of life-like and/or biologically inspired behavior that is robust, repeatable and adaptive. Inspiration from cognitive science, neuroscience and biology drives the development of new methods and models in behavior-based robotics, and the results tie together several related fields including artificial life, evolutionary computation, and multi-agent systems. Ideas from artificial intelligence and engineering continue to be explored actively and applied to behavior-based robots as their role in animal modeling and practical applications is being developed.     

Artificial life: organization, adaptation and complexity from the bottom up

Artificial life attempts to understand the essential general properties of living systems by synthesizing life-like behavior in software, hardware and biochemicals. As many of the essential abstract properties of living systems (e.g. autonomous adaptive and intelligent behavior) are also studied by cognitive science, artificial life and cognitive science have an essential overlap. This review highlights the state of the art in artificial life with respect to dynamical hierarchies, molecular self-organization, evolutionary robotics, the evolution of complexity and language, and other practical applications. It also speculates about future connections between artificial life and cognitive science.     

An embodied model for sensorimotor grounding and grounding transfer: experiments with epigenetic robots

The grounding of symbols in computational models of linguistic abilities is one of the fundamental properties of psychologically plausible cognitive models. In this article, we present an embodied model for the grounding of language in action based on epigenetic robots. Epigenetic robotics is one of the new cognitive modeling approaches to modeling autonomous mental development. The robot model is based on an integrative vision of language in which linguistic abilities are strictly dependent on and grounded in other behaviors and skills. It uses simulated robots that learn through imitation the names of basic actions. Robots also learn higher order action concepts through the process of grounding transfer. The simulation demonstrates how new, higher order behavioral abilities can be autonomously built on previously grounded basic action categories following linguistic interaction with human users.     

Animats and what they can tell us

Animats-autonomous robots or simulations of animals-and the animat approach represent the most recent attempt to comprehend the capacity of animals for autonomous generation of adaptive, intelligent behavior in complex, changing environments. Motivated by perceived limitations in classical artificial intelligence (AI), the animat approach promulgates an alternative, bottom-up route to understanding intelligent behavior. Important tenets include: (1) that adaptive behavior is best understood by focusing on the interaction between a behaving individual and its environment, hence the interest in `embodied' physical robots `situated' in natural environments; (2) that specific abilities, `behaviors', are more natural units of analysis and design than general, information-processing functions and world models; and (3) that high-level behaviors will emerge as systems composed of simple behavioral competences become more complex. Thus, animat research often begins with low-level sensorimotor abilities and then moves up towards higher, cognitive functions. Both in analysis and in design, the animat approach borrows heavily from ethology, psychology, neurobiology and evolutionary biology, as well as from connectionism. For AI and robotics researchers, understanding the mechanisms behind adaptive behavior is secondary to creating them, but natural scientists can hope for tools and concepts to aid understanding of biological systems.     

Is imitation learning the route to humanoid robots?

This review investigates two recent developments in artificial intelligence and neural computation: learning from imitation and the development of humanoid robots. It is postulated that the study of imitation learning offers a promising route to gain new insights into mechanisms of perceptual motor control that could ultimately lead to the creation of autonomous humanoid robots. Imitation learning focuses on three important issues: efficient motor learning, the connection between action and perception, and modular motor control in the form of movement primitives. It is reviewed here how research on representations of, and functional connections between, action and perception have contributed to our understanding of motor acts of other beings. The recent discovery that some areas in the primate brain are active during both movement perception and execution has provided a hypothetical neural basis of imitation. Computational approaches to imitation learning are also described, initially from the perspective of traditional AI and robotics, but also from the perspective of neural network models and statistical-learning research. Parallels and differences between biological and computational approaches to imitation are highlighted and an overview of current projects that actually employ imitation learning for humanoid robots is given.     

Towards a Vygotskyan cognitive robotics: The role of language as a cognitive tool

Cognitive Robotics can be defined as the study of cognitive phenomena by their modeling in physical artifacts such as robots. This is a very lively and fascinating field which has already given fundamental contributions to our understanding of natural cognition. Nonetheless, robotics has to date addressed mainly very basic, low-level cognitive phenomena like sensory-motor coordination, perception, and navigation, and it is not clear how the current approach might scale up to explain high-level human cognition. In this paper we argue that a promising way to do that is to merge current ideas and methods of ‘embodied cognition’ with the Russian tradition of theoretical psychology which views language not only as a communication system but also as a cognitive tool, that is by developing a Vygotskyan cognitive robotics. We substantiate this idea by discussing several domains in which language can improve basic cognitive abilities and permit the development of high-level cognition: learning, categorization, abstraction, memory, voluntary control, and mental life.     

What are symbols symbols of? Situated action,mythological bootstrapping and the emergence of the Self

This paper addresses the question of how symbols should be understood in analytical psychology and psychoanalysis. The point of view examined focuses on the recent turn to more cognitive and developmental models in both disciplines and briefly reviews and critiques the evolutionary and cognitive arguments. The paper then presents an argument based on dynamic systems theory in which no pre-existing template or structure for either mind or behaviour is assumed. Within the dynamic systems model the Self is viewed as an emergent phenomenon deriving from the dynamic patterns existing in a complex system that includes the physiological characteristics of the infant, the intentional attributions of the caregiver and the cultural or symbolic resources that constitute the environment. The symbol can then be seen as a discrete, and in important ways an autonomous, element in the dynamic system. Conclusions are drawn for further research into the nature of the symbol with implications for both theory and practice in analytical psychology and psychoanalysis.     

Robots that imitate humans

The study of social learning in robotics has been motivated by both scientific interest in the learning process and practical desires to produce machines that are useful, flexible, and easy to use. In this review, we introduce the social and task-oriented aspects of robot imitation. We focus on methodologies for addressing two fundamental problems. First, how does the robot know what to imitate? And second, how does the robot map that perception onto its own action repertoire to replicate it? In the future, programming humanoid robots to perform new tasks might be as simple as showing them.     

“Danger,Will Robinson!” The challenges of social robots for intergroup relations

Society's increasing reliance on robots in everyday life provides exciting opportunities for social psychologists to work with engineers in the nascent field of social robotics. In contrast to industrial robots that, for example, may be used on an assembly line, social robots are designed specifically to interact with humans and/or other robots. People tend to perceive social robots as autonomous and capable of having a mind. As such, they are also more likely to be subject to social categorization by humans. As social robots become more human like, people may also feel greater empathy for them and treat robots more like (human) ingroup members. On the other hand, as they become more human like, robots also challenge our human distinctiveness, threaten our identity, and elicit suspicion about their ability to deceive us with their human‐like qualities. We review relevant research to explore this apparent paradox, particularly from an intergroup relations perspective. We discuss these findings and propose three research questions that we believe social psychologists are ideally suited to address.