88 research outputs found
Social-motor experience and perception-action learning bring efficiency to machines
No full textInternational audienc
Data_PlosOne.zip
No full textRaw Data used for the study of unintentional and intentional interpersonal coordination between human subjects and a robo
Simulating the Emergence of Early Physical and Social Interactions : A Developmental Route through Low Level Visuomotor Learning
No full textInternational audienceIn this paper, we propose a bio-inspired and developmental neural model allowing a robot, after learning its own dynamics during a babbling phase, to gain imitative and shape recognition abilities leading to early attempts for physical and social interactions. We use a motor controller based on oscillators. During the babbling step, the robot learn to associate its motor primitives (oscillators) to the visual optical flow induced by its own arm. It also statically learn to recognize its arm by selecting moving local view (feature points) in the visual field. We demonstrate in real indoor experiments that, using this same model, early physical (reaching objects) and social (immediate imitation) interactions can emerge through visual ambiguities induced by the external visual stimuli
Object velocity estimation on images sequences by Hough Transform with projection
No full textInternational audienc
Relaxation markovienne et seuillage par hysteresis pour une detection de mouvement temps reel dans des se- quences couleur
No full textInternational audienc
A Synchrony-Based Perspective for Partner Selection and Attentional Mechanism in Human-Robot Interaction
No full textInternational audienceFuture robots must co-exist and directly interact with human beings. Designing these agents imply solving hard problems linked to human-robot interaction tasks. For instance, how a robot can choose an interacting partner among various agents and how a robot locates regions of interest in its visual field. Studies of neurobiology and psychology collectively named synchrony as an indispensable parameter for social interaction. We assumed that Human-Robot interaction could be initiated by synchrony detection. In this paper, we present a developmental approach for analyzing unintentional synchronization in human-robot interaction. Using our neural network model, the robot learns from a babbling step its inner dynamics by associating its own motor activities (oscillators) with the visual stimulus induced by its own motion. After learning the robot is capable of choosing an interacting agent and of localizing the spatial position of its preferred partner by synchrony detection
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