Emergent imitative behavior on a robotic arm based on visuo-motor associative memories

Abstract

Mimicry and deferred imitation have often been considered as separate kinds of imitation. In this paper, we present a simple architecture for robotic arm control which can be used for both. The model is based on some dynamical equations [1], which provide a motor control with exploring and converging capacities. A visuo-motor map [2] is used to associate positions of the end effector in the visual space with proprioceptive position of the robotic arm. It enables a fast learning of the visuo-motor associations without needing to embed a priori information. The controller can be used both for accurate control and interaction. It has been implemented on a minimal robotic setup showing some interesting emergent properties. The robot can reproduce simple gestures in mimicry situation and finalized actions in deferred imitation situation. Moreover, it can even show some "intention" recognition abilities. Finally, the experiment of deferred imitation which inherits from learning by demonstration, also provides a good basis for cooperative and interactive experiments.

Exploiting the gain-modulation mechanism in parieto-motor neurons: Application to visuomotor transformations and embodied simulation

“…For instance, these works take advantage of self-observation and perceptual ambiguity for the emergence of imitation (Andry, Gaussier, & Nadel, 2002;Andry, Gaussier, Nadel, & Hirsbrunner, 2004;Kuniyoshi, Yorozu, Inaba, & Inoue, 2003), but do not disambiguate the dynamics corresponding to oneself action and those of others (De Rengervé, Boucenna, Andry, & Gaussier, 2010). Among the few exceptions, the works done by Fuke, Ogino, andAsada (2009) andNagai et al (2011) propose to discriminate the motor space with respect to observed actions (self ones or others').…”

Interactive Technologies for Autistic Children: A Review

“…Others studies have also proposed neural network architectures designed to exhibit learning and communication capabilities via imitation [Andry et al, 2001], [Andry et al, 2004], [de Rengervé et al, 2010]. An artificial system does not need to incorporate any other internal model to perform real-time and low-level imitations of human movements despite the related correspondence problem between humans and robots.…”

The effect of learning by imitation on a multi-robot system based on the coupling of low-level imitation strategy and online learning for cognitive map building

“…Our architecture which is based on learning by imitation could be used in many areas. For instance, in [29] authors used this architecture to learn trajectories of the robot's arm. Indeed, the model is based on some dynamical equations which provide a motor control when exploring and converging capacities.…”