Autonomous learning and reproduction of complex sequences: a multimodal architecture for bootstraping imitation games

This paper introduces a control architecture for the learning of complex sequence of gestures applied to autonomous robots. The architecture is designed to exploit the robot internal sensory-motor dynamics generated by visual, proprioceptive, and predictive informations in order to provide intuitive behaviors in the purpose of natural interactions with humans

A Developmental Approach for low-level Imitations

Historically, a lot of authors in psychology and in robotics tend to separate "true imitation" and its related high-level mechanisms which seem to be exclusive to human adult, from low-level imitations or "mimicries" observed on babies or primates. Closely, classical researches suppose that an imitative artificial system must be able to build a model of the demonstrator's geometry, in order to reproduce finely the movements on each joints. Conversely, we will advocate that if imitation is viewed as a part of a developmental course, then (1) an artificial developing system does not need to build any internal model of the other, to perform real-time and low-level imitations of human movements despite the related correspondence problem between man and robot and, (2) a simple sensory-motor loop could be at the basis of multiples heterogeneous imitative behaviors often explained in the literature by different models

From Visuo-Motor Development to Low-level Imitation

We present the first stages of the developmental course of a robot using vision and a 5 degree of freedom robotic arm. During an exploratory behavior, the robot learns visuo-motor control of its mechanical arm. We show how a simple neural network architecture, combining elementary vision, a self-organized algorithm, and dynamical Neural Fields is able to learn and use proper associations between vision and arm movements, even if the problem is ill posed (2-D toward 3-D mapping and also mechanical redundancy between different joints). Highlighting the generic aspect of such an architecture, we show as a robotic result that it is used as a basis for simple gestural imitations of humans. Finally we show how the imitative mechanism carries on the developmental course, allowing the acquisition of more and more complex behavioral capabilities

From Low Level Motor Control to High Level Interaction Skills

The 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

From Low Level Motor Control to High Level Interaction Skills

The 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

A study of two complementary encoding strategies based on learning by demonstration for autonomous navigation task

Learning by demonstration is a natural and interactive way of learning which can be used by non-experts to teach behaviors to robots. In this paper we study two learning by demon- stration strategies which give different an- swers about how to encode information and when to learn. The first strategy is based on artificial Neural Networks and focuses on reactive on-line learning. The second one uses Gaussian Mixture Models built on statistical features extracted off-line from several training datasets. A simple navigation experiment is used to compare the developmental possibilities of each strategy. Finally, they appear to be complementary and we will highlight that each one can be related to a specific memory structure in brain

Modulation and functions of dopamine receptor heteromers in drugs of abuse-induced adaptations

Drug addiction is a chronic and relapsing disorder that leads to compulsive drug intake despite deleterious consequences. By increasing dopamine (DA) in the mesolimbic system, drugs of abuse hijack the brain reward circuitry, which is critical for the development of enduring behavioral alterations. DA mainly acts onto DA D1 (D1R) and D2 (D2R) receptor subtypes, which are positively and negatively coupled to adenylyl cyclase, respectively. Extensive research has aimed at targeting these receptors for the treatment of addiction, however this often results in unwanted side-effects due to the implication of DA receptors in numerous physiological functions. A growing body of evidence indicates that the physical interaction of DA receptors with other receptors can finely tune their function, making DA receptor heteromers promising targets for more specific treatment strategies. An increasing number of articles highlighted the ability of both D1R and D2R to form heteromers, however, most studies carried out to date stem from observations in heterologous systems and the biological significance of DA receptor heteromers in vivo is only emerging. We focused this review on studies that were able to provide insights into functions on D1R and D2R heteromers in drug-evoked adaptations and discuss the limitations of current approaches to study receptor heteromers in vivo. This article is part of the Special Issue entitled 'Receptor heteromers and their allosteric receptor-receptor interactions'.Rôle des heteromères formés par les récepteurs dopamine-glutamate et de signalisation dépendante du calcium nucléaire associée dans l'addictionImpact de la composition lipidique membranaire sur la transmission dopaminergique dépendante du récepteur D2 et la motivationProgram Initiative d’Excellenc

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN