Abstract—There exists a large conceptual gap between symbolic models and emergent models for the mind. Many emergent models work on low-level sensory data, while many symbolic models deal with high-level abstract (i.e., action) symbols. There has been relatively little study on intermediate representations, mainly because of a lack of knowledge about how representations fully autonomously emerge inside the closed brain skull, using information from the exposed two ends (the sensory end and the motor end). As reviewed here, this situation is changing. A fundamental challenge for emergent modelsisabstraction,which symbolic models enjoy through human handcrafting. The term abstract refers to properties disassociated with any particular form. Emergent abstraction seems possible, although the brain appears to never receive a computer symbol (e.g., ASCII code) or produce such a symbol. This paper reviews major agent models with an emphasis on representation. It suggests two different ways to relate symbolic representations with emergent representations: One is based on their categorical definitions. The other considers that a symbolic representation corresponds to a brain’s outside behaviors observed and handcrafted by other outside human observers; but an emergent representation is inside the brain. Index Terms—Agents, attention, brain architecture, complexity, computer vision, emergent representation, graphic models, mental

Abstract—The interaction of robotics with behavioral and cognitive sciences has always been tight. As often described in the literature, the living has inspired the construction of many robots. Yet, in this article, we focus on the reverse phenomenon: building robots can impact importantly the way we conceptualize behavior and cognition in animals and humans. This article presents a series of paradigmatic examples spanning from the modelling of insect navigation, the experimentation of the role of morphology to control locomotion, the development of foundational representations of the body and of the self/other distinction, the self-organization of language in robot societies, and the use of robots as therapeutic tools for children with developmental disorders. Through these examples, I review the way robots can be used as operational models confronting specific theories to reality, or can be used as proof of concepts, or as conceptual exploration tools generating new hypotheses, or used as experimental set ups to uncover particular behavioral properties in animals or humans, or even used as therapeutic tools. Finally, I discuss the fact that in spite of its role in the formation of many fundamental theories in behavioral and cognitive sciences, the use of robots is far from being accepted as a standard tool and contributions are often forgotten, leading to regular rediscoveries and slowing down cumulative progress. The article concludes by highlighting the high priority of further historical and epistemological work. Index Terms—Behavioral and cognitive sciences, development, embodiment, epistemology, modelling, robotics, self-organization, therapeutic tools. I.

Abstract—This paper studies the coupling of internally guided learning and social interaction, and more specifically the improvement owing to demonstrations of the learning by intrinsic motivation. We present Socially Guided Intrinsic Motivation by Demonstration (SGIM-D), an algorithm for learning in continuous, unbounded and non-preset environments. After introducing social learning and intrinsic motivation, we describe the design of our algorithm, before showing through a fishing experiment that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation to gain a wide repertoire while being specialised in specific subspaces. I. APPROACHES FOR ADAPTIVE PERSONAL ROBOTS The promise of personal robots operating in human environments to interact with people on a daily basis points out the importance of adaptivity of the machine to its environment and

This paper focuses on the well-known problem in behavioral robotics – “what to do next”. The problem addressed here lies in the selection of one activity to be executed from multiple regulative, homeostatic and developmental processes running onboard a reconfigurable multi-robot organism. We consider adaptive hardware and software frameworks and argue the non-triviality of action selection for evolutionary robotics. The paper overviews several deliberative, evolutionary and bio-inspired approaches for such an adaptive action selection mechanism.

LEARNING techniques are increasingly being used in today’s complex robotic systems. Robots are expected to deal with a large variety of tasks using their high-dimensional and complex bodies, to manipulate objects and also, to interact with humans in an intuitive and friendly way. In this new setting, not all relevant information is available at design time, and robots should typically be able to learn, through self-experimentation or through human–robot interaction, how to tune their innate perceptual-motor skills or to learn, cumulatively, novel skills that were not preprogrammed initially. In a word, robots need to have the capacity to develop in an open-ended manner and in an open-ended environment, in a way that is analogous to human development which combines genetic and epigenetic factors. This challenge is at the center of the developmental robotics field [7], [35]–[37]. Among the various

Abstract—This is a theoretical, modeling, and algorithmic paper about the spatial aspect of brain-like information processing, modeled by the Developmental Network (DN) model. The new brain architecture allows the external environment (including teachers) to interact with the sensory ends S and the motor ends M of the skull-closed brain B through development. It does not allow the human programmer to hand-pick extra-body concepts or to handcraft the concept boundaries inside the brain B. Mathematically, the brain spatial processing performs real-time mapping from S(t)×B(t)×M(t) to S(t+1)×B(t+1)×M(t+1), through network updates, where the contents of S, B, M all emerge from experience. Using its limited resource, the brain does increasingly better through experience. A new principle is that the effector ends in M serve as hubs for concept learning and abstraction. The effector ends B serve also as input and the sensory ends S serve also as output. As DN embodiments, the Where-What Networks (WWNs) present three major function novelties — new concept abstraction, concept as emergent goals, and goal-directed perception. The WWN series appears to be the first general purpose emergent systems for detecting and recognizing multiple objects in complex backgrounds. Among others, the most significant new mechanism is general-purpose top-down attention. Index Terms—Mental architecture, cortical representation,

Abstract—The interaction of robotics with behavioral and cognitive sciences has always been tight. As often described in the literature, the living has inspired the construction of many robots. Yet, in this article, we focus on the reverse phenomenon: building robots can impact importantly the way we conceptualize behavior and cognition in animals and humans. This article presents a series of paradigmatic examples spanning from the modelling of insect navigation, the experimentation of the role of morphology to control locomotion, the development of foundational representations of the body and of the self/other distinction, the self-organization of language in robot societies, and the use of robots as therapeutic tools for children with developmental disorders. Through these examples, I review the way robots can be used as operational models confronting specific theories to reality, or can be used as proof of concepts, or as conceptual exploration tools generating new hypotheses, or used as experimental set ups to uncover particular behavioral properties in animals or humans, or even used as therapeutic tools. Finally, I discuss the fact that in spite of its role in the formation of many fundamental theories in behavioral and cognitive sciences, the use of robots is far from being accepted as a standard tool and contributions are often forgotten, leading to regular rediscoveries and slowing down cumulative progress. The article concludes by highlighting the high priority of further historical and epistemological work. Index Terms—Behavioral and cognitive sciences, development, embodiment, epistemology, modelling, robotics, self-organization, therapeutic tools. I.

Developmental robotics is a scientific field which aims at studying the developmental mechanisms, architectures and constraints that allow life-long and open-ended learning of new skills and new knowledge in embodied machines. As in human children, learning is expected to be cumulative and of progressively increasing complexity, and to result from self-exploration of the world in combination with social interaction. The typical methodological approach consists in starting from theories of human and animal development elaborated in fields such as developmental psychology, neuroscience, developmental and evolutionary biology, and linguistics, then to formalize and implement them in robots, sometimes exploring extensions or variants of them. The experimentation of those models in robots allows researchers to confront them with reality, and as a consequence developmental robotics also provides feedback and novel hypothesis on theories of human and animal development. Theoretical Background Can a robot learn like a child? Can it learn a variety of new skills and new knowledge unspecified at design time and in a partially unknown and changing environment? How can it discover its body and its relationships with the physical and social environment? How can its cognitive capacities continuously develop without the intervention of an engineer once it is "out of the factory"? What can it learn through natural

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Abstract. Human behavior is complex, but structured along individual and social lines. Robotic systems interacting with people in uncontrolled environments need capabilities to correctly interpret, predict and respond to human behaviors. This paper discusses the scientific, technological and application challenges that arise from the mutual interaction of robotics and computational human behavior understanding. We supply a short survey of the area to provide a contextual framework and describe the most recent research in this area. 1