Vasiliki Vouloutsi

Modulating behaviors using allostatic control

Robots will be part of our society in the future. It is therefore important that they are able to interact with humans in a natural way. This requires the ability to display social competence and behavior that will promote such interactions. Here we present the details of modeling the emergence of emotional states, adaptive internal needs and motivational drives. We explain how this model is enriched by the usage of a homeostatic and allostatic control that aim at regulating its behavior. We evaluate the model during a human-robot interaction and we show how this model is able to produce meaningful and complex behaviors.

Towards the synthetic self: making others perceive me as an other

Abstract Future applications of robotic technologies will involve interactions with non-expert humans as machines will assume the role of companions, teachers or healthcare assistants. In all those tasks social behavior is a key ability that needs to be systematically investigated and modelled at the lowest level, as even a minor inconsistency of the robot’s behavior can greatly affect the way humans will perceive it and react to it. Here we propose an integrated architecture for generating a socially competent robot.We validate our architecture using a humanoid robot, demonstrating that gaze, eye contact and utilitarian emotions play an essential role in the psychological validity or social salience of Human-Robot Interaction (HRI). We show that this social salience affects both the empathic bonding between the human and a humanoid robot and, to a certain extent, the attribution of a Theory of Mind (ToM). More specifically, we investigate whether these social cues affect other utilitarian aspects of the interaction such as knowledge transfer within a teaching context.

Towards a synthetic tutor assistant: The EASEL project and its architecture

Robots are gradually but steadily being introduced in our daily lives. A paramount application is that of education, where robots can assume the role of a tutor, a peer or simply a tool to help learners in a specific knowledge domain. Such endeavor posits specific challenges: affective social behavior, proper modelling of the learner’s progress, discrimination of the learner’s utterances, expressions and mental states, which, in turn, require an integrated architecture combining perception, cognition and action. In this paper we present an attempt to improve the current state of robots in the educational domain by introducing the EASEL EU project. Specifically, we introduce the EASEL’s unified robot architecture, an innovative Synthetic Tutor Assistant (STA) whose goal is to interactively guide learners in a science-based learning paradigm, allowing us to achieve such rich multimodal interactions.

The EASEL Project: Towards Educational Human-Robot Symbiotic Interaction

This paper presents the EU EASEL project, which explores the potential impact and relevance of a robot in educational settings. We present the project objectives and the theorectical background on which the project builds, briefly introduce the EASEL technological developments, and end with a summary of what we have learned from the evaluation studies carried out in the project so far.

Effects of the Robot's Role on Human-Robot Interaction in an Educational Scenario

In order for robots to be part of the education field, it is necessary to take into consideration the perception students have of them and of education in general. The aim of this study is to assess whether the role a robot plays in a classroom affects knowledge retrieval, subjective experience, and the perception of the learners. To investigate this, we developed an educational scenario and three questionnaires. The results show significant differences in the way the subjects perceived the robot as a tutor.

EFAA: a companion emerges from integrating a layered cognitive architecture

In this video, we present the human robot interaction generated by applying the DAC cognitive architecture [1] on the iCub robot. We demonstrate how the robot reacts and adapts to its environment within the context a continuous interactive scenario including different games. We emphasize as well that the artificial agent is maintaining a self-model in terms of emotions and drives and how those are expressed in order affect the social interaction. Categories and Subject Descriptors I.2.9 [Commercial robots and applications]: Layered Cognitive Architecture for Distributed and Adaptive Control of a social robot. General Terms Experimentation, Human Factors, Theory

Moth-Like Chemo-Source Localization and Classification on an Indoor Autonomous Robot

Olfaction is a crucial sense for many living organisms. Many animals, especially insects, rely heavily on the olfactory sense for encoding and processing different chemical cues in order to perform several tasks such as foraging, predator avoidance, mate finding, communication etc.(22). Yet, olfaction has not been as widely studied as vision or the auditory system in insects. At the same time, robotic platforms capable of searching, locating and classifying odor sources in wind turbulence and in the presence of complex odors have diverse applications ranging from environmental monitoring (21), detection of explosives and other hazardous substances (19), land mine detection (2) to human search and rescue operations. The main challenge thereby is the stable and fast coding and decoding of odors and the localization of the sources (17). In our own recent work, we have proposed an insect-like mapless navigation mechanism which integrates surge-and-cast chemo search, path integration, wind detection and visual landmark navigation on an indoor mobile robot (28). Also, we have proposed a model based on insect navigation that is capable of navigating in highly dynamic environments and our model was compared directly to ant navigational data, with strikingly similar navigational behaviors (26). The problem of ambiguous information, particularly in the navigational context, is also addressed in our recent work (27). Beyond that, we have contributed significantly to modeling insect navigation and designing robotic systems such as: a model of the locust Lobula Giant Movement Detector (LGMD) tested on a high speed robot (29), moth-like odor localization for robots (30), control of an unmanned aerial vehicle using a neuronal model of a fly-locust brain (31; 32), moth-like optomotor anemotactic chemical search for robots (33), and a blimp flight control using a biologically inspired flight control system (34). Despite these advances, several biological systems with relatively simple nervous systems solve the odor localization and classification problem much more efficiently than their artificial counterparts: bees use odor to localize nests, ants use pheromone trails to organize foraging in swarms, lobsters use odor to locate food, the Escherichia bacteria use odors to locate nutrients, male moths use olfaction to locate female mates etc. The odor localization

The Influence of Behavioral Complexity on Robot Perception

Since robots’ capabilities increase, they will soon be present in our daily lives and will be required to interact with humans in a natural way. Furthermore, robots will need to be removed from controlled environments and tested in public places where untrained people will be able to freely interact with them. Such needs raise a number of issues: what kind of behaviors are considered important in promoting interaction and how these behaviors affect people’s perception regarding the robot in terms of anthropomorphism, likeability, animacy and perceived intelligence. In this paper, we propose a motivational and emotional system that drives the robot’s behavior and test it against six interaction scenarios of varying complexity. In addition, we evaluate our system in two different environments: a controlled (laboratory) environment and a public space. Results suggest that the perception of the robot significantly changes depending on the complexity of the interaction but does not change depending on the environment.

Effects of Gaze Synchronization in Human-Robot Interaction

In recent years, humanoid robots have been designed to resemble humans and interact with them like human beings interact with each other in a social setting. Joint attention plays an essential role in human-human social interaction. In this study, based on these findings, we want to implement gaze synchronization to a new game scenario, as a part of the communication process between humans and robots. We describe a method, which will be used in order to study the effects of gaze synchronization as a communication channel on human performance and on trustworthiness during a cooperative cognitive task between humans and a robot.

Insect-Like Odor Classification and Localization on an Autonomous Robot

The study of natural olfaction can assist in developing more robust and sensitive artificial chemical sensing systems. Here we present the implementation on an indoor fully autonomous wheeled robot of two insect models for odor classification and localization based on moth behavior and the insect’s olfactory pathway. Using the biologically based signal encoding scheme of the Temporal Population Code (TPC) as a model of the antenna lobe, the robot is able to identify and locate the source of odors using real-time chemosensor signals. The results of the tests performed show a successful classification for ethanol and ammonia under controlled conditions. Moreover, a comparison between the results obtained with and without the localization algorithm, shows an effect of the behavior itself on the performance of the classifier, suggesting that the behavior of insects may be optimized for the specific sensor encoding scheme they deploy in odor discrimination.