Towards a roadmap for living machines

A roadmap is a plan that identifies short-term and long-term goals of a research area and suggests potential ways in which those goals can be met. This roadmap is based on collating answers from interview with experts in the field of biomimetics, and covers a broad range of specialties. Interviews were carried out at events organized by the Convergent Science Network, including a workshop on biomimetics and Living Machines 2012. We identified a number of areas of strategic importance, from biomimetic air and underwater vehicles, to robot designs based on animal bodies, to biomimetic technologies for sensing and perception. © 2013 Springer-Verlag Berlin Heidelberg

Optimising robot personalities for symbiotic interaction

The Expressive Agents for Symbiotic Education and Learning (EASEL) project will explore human-robot symbiotic interaction (HRSI) with the aim of developing an understanding of symbiosis over long term tutoring interactions. The EASEL system will be built upon an established and neurobiologically grounded architecture - Distributed Adaptive Control (DAC). Here we present the design of an initial experiment in which our facially expressive humanoid robot will interact with children at a public exhibition. We discuss the range of measurements we will employ to explore the effects our robot's expressive ability has on interaction with children during HRSI, with the aim of contributing optimal robot personality parameters to the final EASEL model. © 2014 Springer International Publishing

Embodied Models and Neurorobotics

Neuroscience has become a very broad field indeed: each year around 30,000 researchers and students from around the ... We trace a path from neuron to cognition via computational neuroscience, but what is computational neuroscience

Expression of emotional states during locomotion based on canonical parameters

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Analyzing children's expectations from robotic companions in educational settings

The use of robots as educational partners has been extensively explored, but less is known about the required characteristics these robots should have to meet children's expectations. Thus the purpose of this study is to analyze children's assumptions regarding morphology, functionality, and body features, among others, that robots should have to interact with them. To do so, we analyzed 142 drawings from 9 to 10 years old children and their answers to a survey provided after interacting with different robotic platforms. The main results convey on a gender-less robot with anthropomorphic (but machine-like) characteristics

Robot companions for citizens

This paper describes the scientific vision and objectives of the FET Flagship candidate initiative Robot Companions for Citizens. Robot Companions will be a new generation of machines that will primarily help and assist elderly people in activities of daily living in their workplace, home and in society. They will be the ICT solution for a new sustainable welfare

Towards a synthetic tutor assistant: The EASEL project and its architecture

Robots are gradually but steadily being introduced in our daily lives. A paramount application is that of education, where robots can assume the role of a tutor, a peer or simply a tool to help learners in a specific knowledge domain. Such endeavor posits specific challenges: affective social behavior, proper modelling of the learner’s progress, discrimination of the learner’s utterances, expressions and mental states, which, in turn, require an integrated architecture combining perception, cognition and action. In this paper we present an attempt to improve the current state of robots in the educational domain by introducing the EASEL EU project. Specifically, we introduce the EASEL’s unified robot architecture, an innovative Synthetic Tutor Assistant (STA) whose goal is to interactively guide learners in a science-based learning paradigm, allowing us to achieve such rich multimodal interactions

DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users

A Living Machines approach to the sciences of mind and brain

How do the sciences of mind and brain—neuroscience, psychology, cognitive science, and artificial intelligence (AI)—stand in relation to each other in the 21st century? This chapter proposes that despite our knowledge expanding at ever-accelerating rates, our understanding of the relationship between mind and brain is, in some important sense, becoming less and less. An increasing explanatory gap can only be bridged by a multi-tiered and integrated theoretical framework that recognizes the value of developing explanations at different levels, combining these into cross-level integrated theories, and directly contributing to new technologies that improve the human condition. Development of technologies that instantiate principles gleaned from the study of the mind and brain, or biomimetic technologies, is a key part of the validation process for scientific theories of mind and brain. We call this strategy for the integration of science and engineering a Living Machines approach. Following this path can lead not only to better science, and useful engineering, but also a richer view of human experience and of relationships between science, engineering, and art

Adaptive Fields: Distributed Representations of Classically Conditioned Associations

Present neural models of classical conditioning all suffer from the same shortcoming: local representation of information (therefore, very precise neural prewiring is necessary). As an alternative we develop two neural models of classical conditioning which rely on distributed representations of information. Both models are of the Hopfield type. In the first model the existence of transmission delays is used to store temporal relations. The second model is based on interactions between spatially separated neural fields. Using tools from statistical mechanics we show that behavioural constraints can be met only if the Hebb rule is extended with inter- or intrasynaptic competition. 2 3 1. Introduction Connectionism has redirected the attention of cognitive scientists to learning and to the neural substrate in which cognitive processes are implemented. Conditioning has become an important field in which ideas from neural networks, behavioural science and neurophysiology are combined. ..