Interaction Histories and Short-Term Memory: Enactive Development of Turn-Taking Behaviours in a Childlike Humanoid Robot

In this article, an enactive architecture is described that allows a humanoid robot to learn to compose simple actions into turn-taking behaviours while playing interaction games with a human partner. The robot’s action choices are reinforced by social feedback from the human in the form of visual attention and measures of behavioural synchronisation. We demonstrate that the system can acquire and switch between behaviours learned through interaction based on social feedback from the human partner. The role of reinforcement based on a short-term memory of the interaction was experimentally investigated. Results indicate that feedback based only on the immediate experience was insufficient to learn longer, more complex turn-taking behaviours. Therefore, some history of the interaction must be considered in the acquisition of turn-taking, which can be efficiently handled through the use of short-term memory.Peer reviewedFinal Published versio

Towards a Cognitive Architecture for Socially Adaptive Human-Robot Interaction

People have a natural predisposition to interact in an adaptive manner with others, by instinctively changing their actions, tones and speech according to the perceived needs of their peers. Moreover, we are not only capable of registering the affective and cognitive state of our partners, but over a prolonged period of interaction we also learn which behaviours are the most appropriate and well-suited for each one of them individually. This universal trait that we share regardless of our different personalities is referred to as social adaptation (adaptability). Humans are always capable of adapting to the others although our personalities may influence the speed and efficacy of the adaptation. This means that in our everyday lives we are accustomed to partake in complex and personalized interactions with our peers. Carrying this ability to personalize to human-robot interaction (HRI) is highly desirable since it would provide user-personalized interaction, a crucial element in many HRI scenarios - interactions with older adults, assistive or rehabilitative robotics, child-robot interaction (CRI), and many others. For a social robot to be able to recreate this same kind of rich, human-like interaction, it should be aware of our needs and affective states and be capable of continuously adapting its behaviour to them. Equipping a robot with these functionalities however is not a straightforward task. A robust approach for solving this is implementing a framework for the robot supporting social awareness and adaptation. In other words, the robot needs to be equipped with the basic cognitive functionalities, which would allow the robot to learn how to select the behaviours that would maximize the pleasantness of the interaction for its peers, while being guided by an internal motivation system that would provide autonomy to its decision-making process. The goal of this research was threefold: attempt to design a cognitive architecture supporting social HRI and implement it on a robotic platform; study how an adaptive framework of this kind would function when tested in HRI studies with users; and explore how including the element of adaptability and personalization in a cognitive framework would in reality affect the users - would it bring an additional richness to the human-robot interaction as hypothesized, or would it instead only add uncertainty and unpredictability that would not be accepted by the robot`s human peers? This thesis covers the work done on developing a cognitive framework for human-robot interaction; analyzes the various challenges of implementing the cognitive functionalities, porting the framework on several robotic platforms and testing potential validation scenarios; and finally presents the user studies performed with the robotic platforms of iCub and MiRo, focused on understanding how a cognitive framework behaves in a free-form HRI context and if humans can be aware and appreciate the adaptivity of the robot. In summary, this thesis had the task of approaching the complex field of cognitive HRI and attempt to shed some light on how cognition and adaptation develop from both the human and the robot side in an HRI scenario

Humanoid-based protocols to study social cognition

Social cognition is broadly defined as the way humans understand and process their interactions with other humans. In recent years, humans have become more and more used to interact with non-human agents, such as technological artifacts. Although these interactions have been restricted to human-controlled artifacts, they will soon include interactions with embodied and autonomous mechanical agents, i.e., robots. This challenge has motivated an area of research related to the investigation of human reactions towards robots, widely referred to as Human-Robot Interaction (HRI). Classical HRI protocols often rely on explicit measures, e.g., subjective reports. Therefore, they cannot address the quantification of the crucial implicit social cognitive processes that are evoked during an interaction. This thesis aims to develop a link between cognitive neuroscience and human-robot interaction (HRI) to study social cognition. This approach overcomes methodological constraints of both fields, allowing to trigger and capture the mechanisms of real-life social interactions while ensuring high experimental control. The present PhD work demonstrates this through the systematic study of the effect of online eye contact on gaze-mediated orienting of attention. The study presented in Publication I aims to adapt the gaze-cueing paradigm from cognitive science to an objective neuroscientific HRI protocol. Furthermore, it investigates whether the gaze-mediated orienting of attention is sensitive to the establishment of eye contact. The study replicates classic screen-based findings of attentional orienting mediated by gaze both at behavioral and neural levels, highlighting the feasibility and the scientific value of adding neuroscientific methods to HRI protocols. The aim of the study presented in Publication II is to examine whether and how real-time eye contact affects the dual-component model of joint attention orienting. To this end, cue validity and stimulus-to-onset asynchrony are also manipulated. The results show an interactive effect of strategic (cue validity) and social (eye contact) top-down components on the botton-up reflexive component of gaze-mediated orienting of attention. The study presented in Publication III aims to examine the subjective engagement and attribution of human likeness towards the robot depending on established eye contact or not during a joint attention task. Subjective reports show that eye contact increases human likeness attribution and feelings of engagement with the robot compared to a no-eye contact condition. The aim of the study presented in Publication IV is to investigate whether eye contact established by a humanoid robot affects objective measures of engagement (i.e. joint attention and fixation durations), and subjective feelings of engagement with the robot during a joint attention task. Results show that eye contact modulates attentional engagement, with longer fixations at the robot’s face and cueing effect when the robot establishes eye contact. In contrast, subjective reports show that the feeling of being engaged with the robot in an HRI protocol is not modulated by real-time eye contact. This study further supports the necessity for adding objective methods to HRI. Overall, this PhD work shows that embodied artificial agents can advance the theoretical knowledge of social cognitive mechanisms by serving as sophisticated interactive stimuli of high ecological validity and excellent experimental control. Moreover, humanoid-based protocols grounded in cognitive science can advance the HRI community by informing about the exact cognitive mechanisms that are present during HRI

DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self

This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users

Live human-robot interactive public demonstrations with automatic emotion and personality prediction.

Communication with humans is a multi-faceted phenomenon where the emotions, personality and non-verbal behaviours, as well as the verbal behaviours, play a significant role, and human-robot interaction (HRI) technologies should respect this complexity to achieve efficient and seamless communication. In this paper, we describe the design and execution of five public demonstrations made with two HRI systems that aimed at automatically sensing and analysing human participants' non-verbal behaviour and predicting their facial action units, facial expressions and personality in real time while they interacted with a small humanoid robot. We describe an overview of the challenges faced together with the lessons learned from those demonstrations in order to better inform the science and engineering fields to design and build better robots with more purposeful interaction capabilities. This article is part of the theme issue 'From social brains to social robots: applying neurocognitive insights to human-robot interaction'.EPSR

Shared Perception in Human-Robot Interaction

Interaction can be seen as a composition of perspectives: the integration of perceptions, intentions, and actions on the environment two or more agents share. For an interaction to be effective, each agent must be prone to “sharedness”: being situated in a common environment, able to read what others express about their perspective, and ready to adjust one’s own perspective accordingly. In this sense, effective interaction is supported by perceiving the environment jointly with others, a capability that in this research is called Shared Perception. Nonetheless, perception is a complex process that brings the observer receiving sensory inputs from the external world and interpreting them based on its own, previous experiences, predictions, and intentions. In addition, social interaction itself contributes to shaping what is perceived: others’ attention, perspective, actions, and internal states may also be incorporated into perception. Thus, Shared perception reflects the observer's ability to integrate these three sources of information: the environment, the self, and other agents. If Shared Perception is essential among humans, it is equally crucial for interaction with robots, which need social and cognitive abilities to interact with humans naturally and successfully. This research deals with Shared Perception within the context of Social Human-Robot Interaction (HRI) and involves an interdisciplinary approach. The two general axes of the thesis are the investigation of human perception while interacting with robots and the modeling of robot’s perception while interacting with humans. Such two directions are outlined through three specific Research Objectives, whose achievements represent the contribution of this work. i) The formulation of a theoretical framework of Shared Perception in HRI valid for interpreting and developing different socio-perceptual mechanisms and abilities. ii) The investigation of Shared Perception in humans focusing on the perceptual mechanism of Context Dependency, and therefore exploring how social interaction affects the use of previous experience in human spatial perception. iii) The implementation of a deep-learning model for Addressee Estimation to foster robots’ socio-perceptual skills through the awareness of others’ behavior, as suggested in the Shared Perception framework. To achieve the first Research Objective, several human socio-perceptual mechanisms are presented and interpreted in a unified account. This exposition parallels mechanisms elicited by interaction with humans and humanoid robots and aims to build a framework valid to investigate human perception in the context of HRI. Based on the thought of D. Davidson and conceived as the integration of information coming from the environment, the self, and other agents, the idea of "triangulation" expresses the critical dynamics of Shared Perception. Also, it is proposed as the functional structure to support the implementation of socio-perceptual skills in robots. This general framework serves as a reference to fulfill the other two Research Objectives, which explore specific aspects of Shared Perception. For what concerns the second Research Objective, the human perceptual mechanism of Context Dependency is investigated, for the first time, within social interaction. Human perception is based on unconscious inference, where sensory inputs integrate with prior information. This phenomenon helps in facing the uncertainty of the external world with predictions built upon previous experience. To investigate the effect of social interaction on such a mechanism, the iCub robot has been used as an experimental tool to create an interactive scenario with a controlled setting. A user study based on psychophysical methods, Bayesian modeling, and a neural network analysis of human results demonstrated that social interaction influenced Context Dependency so that when interacting with a social agent, humans rely less on their internal models and more on external stimuli. Such results are framed in Shared Perception and contribute to revealing the integration dynamics of the three sources of Shared Perception. The others’ presence and social behavior (other agents) affect the balance between sensory inputs (environment) and personal history (self) in favor of the information shared with others, that is, the environment. The third Research Objective consists of tackling the Addressee Estimation problem, i.e., understanding to whom a speaker is talking, to improve the iCub social behavior in multi-party interactions. Addressee Estimation can be considered a Shared Perception ability because it is achieved by using sensory information from the environment, internal representations of the agents’ position, and, more importantly, the understanding of others’ behavior. An architecture for Addressee Estimation is thus designed considering the integration process of Shared Perception (environment, self, other agents) and partially implemented with respect to the third element: the awareness of others’ behavior. To achieve this, a hybrid deep-learning (CNN+LSTM) model is developed to estimate the speaker-robot relative placement of the addressee based on the non-verbal behavior of the speaker. Addressee Estimation abilities based on Shared Perception dynamics are aimed at improving multi-party HRI. Making robots aware of other agents’ behavior towards the environment is the first crucial step for incorporating such information into the robot’s perception and modeling Shared Perception

Towards engagement models that consider individual factors in HRI: on the relation of extroversion and negative attitude towards robots to gaze and speech during a human-robot assembly task : Experiments with the iCub humanoid robot

International audienceEstimating the engagement is critical for human-robot interaction. Engagement measures typically rely on the dynamics of the social signals exchanged by the partners, especially speech and gaze. However, the dynamics of these signals are likely to be influenced by individual and social factors, such as personality traits, as it is well documented that they critically influence how two humans interact with each other. Here, we assess the influence of two factors, namely extroversion and negative attitude toward robots, on speech and gaze during a cooperative task, where a human must physically manipulate a robot to assemble an object. We evaluate if the score of extroversion and negative attitude towards robots co-variate with the duration and frequency of gaze and speech cues. The experiments were carried out with the humanoid robot iCub and N=56 adult participants. We found that the more people are extrovert, the more and longer they tend to talk with the robot; and the more people have a negative attitude towards robots, the less they will look at the robot face and the more they will look at the robot hands where the assembly and the contacts occur. Our results confirm and provide evidence that the engagement models classically used in human-robot interaction should take into account attitudes and personality traits

Prédiction multi-modale à l'aide d'apprentissage PRObabiliste de Mouvement Primitives

International audienceThis paper proposes a method for multi-modal prediction of intention based on a probabilistic description of movement primitives and goals. We target dyadic interaction between a human and a robot in a collaborative scenario. The robot acquires multi-modal models of collaborative action primitives containing gaze cues from the human partner and kinetic information about the manipulation primitives of its arm. We show that if the partner guides the robot with the gaze cue, the robot recognizes the intended action primitive even in the case of ambiguous actions. Furthermore, this prior knowledge acquired by gaze greatly improves the prediction of the future intended trajectory during a physical interaction. Results with the humanoid iCub are presented and discussed.Dans ce papier, nous proposons une méthode de prédiction multi-modale de l'intention basé sur une description probabiliste de primitives de mouvements et de buts. On s'interesse ici à un scénario d'interaction collaborative entre un humain et un robot. Le robot modelise l'action collaborative de manière multi-modale, à l'aide de primitives contenant des informations visuelles (orientation du regard du partenaire) ainsi que des informations sur la dynamique de ses propre bras. Nous montrons dans cette étude que si le partenaire guide le robot en utilisant son regard, le robot reconnait l'action attendu par le partenaire et ce, même dans le cas où les mouvements sont ambigus. Nous montrons aussi qu'en guidant le début du mouvement du robot physiquement , le robot peut même afiner sa trajectoire pour respecter encore mieux la volonté de son partenaire. Finalement, en utilisant les deux modalités, le robot peut utiliser l'information visuelle comme un a-priori sur l'action a effectuer, ce qui permet d'améliorer la reconnaissance de la trajectoire attendue lors d'interaction physique