Search

International audience

International audience; Recent psychological experiments intend to show that social intentions can be read from the recording of motor actions (Becchio, Sartori, and Castiello 2010; Ferri et al. 2011). At the center of the debate is the hypothesis that the motor system is (Blackemore and Decety 2001), or is not (Jacob and Jeannerod 2005) used to recognize social intentions , with a potential openning to a bottom-up understanding of social behavior, agentivity and theory of mind. In (Becchio et al. 2007), the authors proposed to record the arm's trajectories during episodes of a "pick and place" task with a motor vs social outcome. The results provided evidence for differences in motor patterning depending on the social context and intention, but where not yet a direct evidence of the involvement of the motor system in recognizing social intention. In (Becchio, Sartori, and Castiello 2010; Ferri et al. 2011), the authors show how social affordances can change the movement parametrization with the hypothesis that a same action linked to a social context may involve an increase of the index of difficulty. Such experiments raise the issue of understanding an-ticipatory motor control and how the recognition of social situations affects at a very low level the generation of motor trajectories, and conversely, how trajectories, as a trace of intentions, can affect the social environement. In this paper, we present a pluridsciplinary 1 , study dedicated to understand the link between anticipatory motor control and motor intentions. Our goal is to propose a control architecture for a humanoid robot based on hydraulic technology (Fig. 1), with a potential of high degree of compliance. We have first conducted psychological experiments (Lewkowicz et al. 2013) designed to record the kinematic of the hand of human subjects during a two player's game (similar to the jungle speed game,Fig. 3) involving an object and 3 condition of play (moving the object 1) to play, 2) for "me", 3) for the other player). In these studies, we have confirmed that typical velocity profiles are affected by the subject's motor intentions according to the final destination of the object. Interestingly, we also developed in this study a simple feedforward NN

National audience; Présentation durant les JNRH 2014 des travaux du laboratoire ETIS concernant l'apprentissage de trajectoire motrice via un modèle de réseau de neurone de type Per-Ac.

International audience; This paper tries to analyze and evaluate emergent structures in a multi-agent system which is able to resolve the warehouse location problem. These emergent structures allow agents to optimize their planning time and to improve their adaptive behavior in an unknown environment. In our multi-agent system, each agent is based on an on-line building and learning of its own cognitive map. It alters the positive impact of individual behavior in the improvement of the overall performance of the system. We also suggest the evaluation of the emergent structures by comparing the performance of our multi-agent system with a linear programming approach. A series of simulations enables us to discuss and validate our system.

In this work, we are interested in understanding how emotional interactions with a social partner can bootstrap increasingly complex behaviors such as social referencing. Our idea is that social referencing as well as facial expression recognition can emerge from a simple sensori-motor system involing emotional stimuli. Without knowing that the other is an agent, the robot is able to learn some complex tasks if the human partner has some "empathy" or at least "resonate" with the robot head (low level emotional resonance). Hence we advocate the idea that social referencing can be boostrapped from a simple sensori-motor system not dedicated to social interactions.

International audience; In this work, we are interesting in understanding how emotional interactions with a social partner can bootstrap increasingly complex behaviors, which is important both for robotics application and un- derstanding development. In particular, we pro- pose that social referencing, gathering information through emotional interaction, fulfills this goal. So- cial referencing, a developmental process incorporat- ing the ability to recognize, understand, respond to and alter behavior in response to the emotional ex- pressions of a social partner, allows an infant, or a robot, to seek information from another individual and use that information to guide his behavior to- ward an object or event

International audience; The proposed work raises the problem of autonomous learnings in robotics. Whatever the morphology of the robots and the various skills they could acquire according to their morphology, robots need to be able to self-evaluate, not only in order to guide their autonomous development trough the vast sensory-motor space, but also in order to verify that previous learning are still pertinent and to readapt their knowledge when previous learnings becomes erroneous. Such capabilities appear crucial in developmental robotics and should largely enrich the possible manifolds of social and physical interactions in which robots could be involved. As a support to this assumption, the presentation will focus on a mature bio-inspired neural network architecture, which uses an autonomous, online, and interactive learning, allowing a robot to achieve sensory-motor tasks and planning, in the frame of navigation. The role of self-evaluation for interactive learnings and self-development is highlighted

International audience; We are interested in knowing how a robot head can learn to recognize facial expressions without supervision. Our starting point is a mathematical model showing that a sensory-motor architecture is able to express its emotions succeedes to recognize on-line the facial expression of a caregiver if this latter naturally tends to imitate or to resonate with the system. Interestingly, our works also show that, learning autonomously to recognize a face/non face is more complex than to recognize a facial expression. We propose an architecture using the interaction rhythm to allow first a robust learning of the facial expression without a face tracking and next to perform the learning of the face/non face recognition. Finally we emphasize the importance of the emotions as a mechanism to ensure the dynamical coupling between individuals allowing to learn more and more complex tasks.

International audience; We are interested in knowing how a robot head can learn to recognize facial expressions without su- pervision. Our starting point is a mathematical model showing that a sensory-motor architecture is able to express its emotions succeedes to recognize on-line the facial expression of a caregiver if this latter naturally tends to imitate or to resonate with the system.

International audience