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Reachable by walking: inappropriate integration of near and far space may lead to distance errors
Get PDFOur experimental results show that infants while learning to walk intend to reach for unreachable objects. These distance errors may result from inappropriate integration of reaching and locomotor actions, attention control and near/far visual space. Infants during their first months are fairly immobile, their attention and actions are constrained to near (reachable) space. Walking, in contrast, lures attention to distal displays and provides the information to disambiguate far space. In this paper, we make use of a reward-mediated learning to mimic the development of absolute distance perception. The results obtained with the NAO robot support further our hypothesis that the representation of near space changes after the onset of walking, which may cause the occurrence of distance errors
From Low Level Motor Control to High Level Interaction Skills
Get PDFThe goal of this research is to create a non-verbal system able to interact safely and naturally with humans. The main hypothesis is that mechanisms of high level interactions such as cooperation and understanding intentions can be obtained from well designed low-level systems. For example, an effector device instrumented to detect force constraints applied by others allows to get easily the direction (opposing vs facilitating) and, at a higher level of interpretation, the intention of others concerning the device's movement. This is one of the reasons we preferred hydraulic technology which presents a potential of physical compliance. Moreover, pressure control in the pistons is closer to muscles control than the electric motors. For the control architecture, we are interested in modeling the layers of motor command : low level force control, multimodal inputs (especially vision) leading to prediction and anticipation capabilities. To do so, this research includes the design of a bio-inspired neural network able to provide a force control of the hardware and merging inputs from different kind of sensors including vision and proprioception. The control has to be as close as possible to the hardware with the less layer possible. It is based on a control by activation of agonist and antagonist muscles. The position and torque sensor as well as short range proximity sensor are used to learn simple movements and their sensory outcome. The vision is also available through robotic eye mounted on a fast pan-tilt system allowing movement at human speed. High definition camera gives a video flow that can be used to analyze the scene. The neural network designed allows the system to analyze the scene using point of interest. By extracting local features around those points it is possible to construct a library of visual feature. Using this library objects can be recognize by learning simple associations between those local feature and sensorial context including supervision signals. Action can then be associated with the context or the presence of an object. Moreover sequences of simple actions can be learned through cognitive maps. For example the robot can learn from the human teacher to grasp, move and release an object. From then and with the recognition of object the robot is able to learn tasks such as sorting objects using their visual characteristic. As we construct this controller we hope to improve our knowledge of some structures of the brain such as the motor cortex, the pre-frontal cortex, the striatum or the cerebellum. Models of all these structures and other are used in the model here developed. The researches aim especially to better understand the influence of each structure on the global behavior of the robot as well as the synergies that emerge from the cooperation between structures and to create a new type of humanoid robot where all parts from the technology, through the low level control to the high level control is thought in the optic of realistic interactions with humans
Whom Will an Intrinsically Motivated Robot Learner Choose to Imitate from?
Get PDFThis paper studies an interactive learning system that couples internally guided learning and social interaction in the case it can interact with several teachers. Socially Guided Intrinsic Motivation with Interactive learning at the Meta level (SGIMIM) is an algorithm for robot learning of motor skills in highdimensional, continuous and non-preset environments, with two levels of active learning: SGIM-IM actively decides at a metalevel when and to whom to ask for help; and an active choice of goals in autonomous exploration. We illustrate through an air hockey game that SGIM-IM efficiently chooses the best strategy
Trends in wissenschaftllichen Publikationsformaten / Repräsentation mehrteiliger Publikationen am Beispiel Universität Bielefeld
No full textDie Entwicklung der Nutzung elektronischer Zeitschriften an der Universität Bielefeld: eine statistische Auswertung
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Information und/oder Infrastruktur - Transformation gestalten: Wünsche, Herausforderung, Realität
Get PDFSondertermin zur Verabschiedung des ltd. Direktors der Universitätsbibliothek Bielefeld Dr. Michael Höppne
Emergence of Leadership in a Group of Autonomous Robots
Get PDFFor modern biology and ethology, the reason for the emergence of leaders-followers patterns in groups of living organisms, is the need of social coordination. In this paper we attempt to examine factors contributing to the emergence of leadership, trying to understand the relation between leader role and behavioral capabilities. In order to achieve this goal, we use a simulation technique where a group of foraging robots has to choose between two identical food zones. Thus, robots must coordinate in some way in order to select the same food zone and collectively gathering food. Behavioral and quantitative analysis indicate that a form of leadership emerges and the emergence of leadership relates with high level of fitness. Moreover, we show that more skilled individuals in a group tend to assume a leadership role, in agreement with literature
Emergent Spontaneous Movements Based on Embodiment: Toward a General Principle for Early Development
Get PDFWe investigate whether spontaneous movements, which initiate and guide early development in animals, can be accounted for by the properties underlying embodiment. We constructed computer and robotic models of several biological species with biologically plausible musculoskeletal bodies and nervous systems, and extracted the embodied and motor networks based on inter-muscle connectivities. In computer simulations and robot experiments, we found that the embodied and motor networks had similar global and local topologies, suggesting the key role of embodiment in generating spontaneous movements in animals