Arousal regulation and affective adaptation to human responsiveness by a robot that explores and learns a novel environment

Copyright © 2014 Hiolle, Lewis and Cañamero. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.In the context of our work in developmental robotics regarding robot-human caregiver interactions, in this paper we investigate how a "baby" robot that explores and learns novel environments can adapt its affective regulatory behavior of soliciting help from a "caregiver" to the preferences shown by the caregiver in terms of varying responsiveness. We build on two strands of previous work that assessed independently (a) the differences between two "idealized" robot profiles-a "needy" and an "independent" robot-in terms of their use of a caregiver as a means to regulate the "stress" (arousal) produced by the exploration and learning of a novel environment, and (b) the effects on the robot behaviors of two caregiving profiles varying in their responsiveness-"responsive" and "non-responsive"-to the regulatory requests of the robot. Going beyond previous work, in this paper we (a) assess the effects that the varying regulatory behavior of the two robot profiles has on the exploratory and learning patterns of the robots; (b) bring together the two strands previously investigated in isolation and take a step further by endowing the robot with the capability to adapt its regulatory behavior along the "needy" and "independent" axis as a function of the varying responsiveness of the caregiver; and (c) analyze the effects that the varying regulatory behavior has on the exploratory and learning patterns of the adaptive robot.Peer reviewe

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

A Developmental Approach to the Study of Affective Bonds for Human-Robot Interaction

Robotics agents are meant to play an increasingly larger role in our everyday lives. To be successfully integrated in our environment, robots will need to develop and display adaptive, robust, and socially suitable behaviours. To tackle these issues, the robotics research community has invested a considerable amount of efforts in modelling robotic architectures inspired by research on living systems, from ethology to developmental psychology. Following a similar approach, this thesis presents the research results of the modelling and experimental testing of robotic architectures based on affective and attachment bonds between young infants and their primary caregiver. I follow a bottom-up approach to the modelling of such bonds, examining how they can promote the situated development of an autonomous robot. Specifically, the models used and the results from the experiments carried out in laboratory settings and with naive users demonstrate the impact such affective bonds have on the learning outcomes of an autonomous robot and on the perception and behaviour of humans. This research leads to the emphasis on the importance of the interplay between the dynamics of the regulatory behaviours performed by a robot and the responsiveness of the human partner. The coupling of such signals and behaviours in an attachment-like dyad determines the nature of the outcomes for the robot, in terms of learning or the satisfaction of other needs. The experiments carried out also demonstrate of the attachment system can help a robot adapt its own social behaviour to that of the human partners, as infants are thought to do during their development

A systematic literature review of decision-making and control systems for autonomous and social robots

In the last years, considerable research has been carried out to develop robots that can improve our quality of life during tedious and challenging tasks. In these contexts, robots operating without human supervision open many possibilities to assist people in their daily activities. When autonomous robots collaborate with humans, social skills are necessary for adequate communication and cooperation. Considering these facts, endowing autonomous and social robots with decision-making and control models is critical for appropriately fulfiling their initial goals. This manuscript presents a systematic review of the evolution of decision-making systems and control architectures for autonomous and social robots in the last three decades. These architectures have been incorporating new methods based on biologically inspired models and Machine Learning to enhance these systems’ possibilities to developed societies. The review explores the most novel advances in each application area, comparing their most essential features. Additionally, we describe the current challenges of software architecture devoted to action selection, an analysis not provided in similar reviews of behavioural models for autonomous and social robots. Finally, we present the future directions that these systems can take in the future.The research leading to these results has received funding from the projects: Robots Sociales para Estimulación Física, Cognitiva y Afectiva de Mayores (ROSES), RTI2018-096338-B-I00, funded by the Ministerio de Ciencia, Innovación y Universidades; Robots sociales para mitigar la soledad y el aislamiento en mayores (SOROLI), PID2021-123941OA-I00, funded by Agencia Estatal de Investigación (AEI), Spanish Ministerio de Ciencia e Innovación. This publication is part of the R&D&I project PLEC2021-007819 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR

Computational models of attachment and self-attachment

We explore, using a variety of models grounded in computational neuroscience, the dynamics of attachment formation and change. In the first part of the thesis we consider the formation of the traditional organised forms of attachment (as defined by Mary Ainsworth) within the context of the free energy principle, showing how each type of attachment might arise in infant agents who minimise free energy over interoceptive states while interacting with caregivers with varying responsiveness. We show how exteroceptive cues (in the form of disrupted affective communication from the caregiver) can result in disorganised forms of attachment (as first uncovered by Mary Main) in infants of caregivers who consistently increase stress on approach, but can have an organising (towards ambivalence) effect in infants of inconsistent caregivers. The second part of the thesis concerns Self-Attachment: a new self-administrable attachment-based psychotherapy recently introduced by Abbas Edalat, which aims to induce neural plasticity in order to retrain an individual's suboptimal attachment schema. We begin with a model of the hypothesised neurobiological underpinnings of the Self-Attachment bonding protocols, which are concerned with the formation of an abstract, self-directed bond. Finally, using neuroscientific findings related to empathy and the self-other distinction within the context of pain, we propose a simple spiking neural model for how empathic states might serve to motivate application of the aforementioned bonding protocols.Open Acces

The Long-Term Efficacy of “Social Buffering” in Artificial Social Agents: Contextual Affective Perception Matters

© 2022 Khan and Cañamero. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). https://creativecommons.org/licenses/by/4.0/In dynamic (social) environments, an affective state of “stress” can be adaptive and promote agent wellbeing, but maladaptive if not appropriately regulated. The presence of (and interactions with) affect-based social support has been hypothesised to provide mechanisms to regulate stress (the “social buffering” hypothesis), though the precise, underlying mechanisms are still unclear. However, the hormone oxytocin has been implicated in mediating these effects in at least two ways: by improving social appraisals and reducing the short-term release of stress hormones (i.e., cortisol), and adapting an agent’s long-term stress tolerance. These effects likely facilitate an agent’s long-term adaptive ability by grounding their physiological and behavioural adaptation in the (affective) social environment, though these effects also appear to be context-dependent. In this paper, we investigate whether two of the hypothesised hormonal mechanisms that underpin the “social buffering” phenomenon affect the long-term wellbeing of (artificial) social agents who share affective social bonds, across numerous social and physical environmental contexts. Building on previous findings, we hypothesise that “social buffering” effects can improve the long-term wellbeing of agents who share affective social bonds in dynamic environments, through regular prosocial interactions with social bond partners. We model some of the effects associated with oxytocin and cortisol that underpin these hypothesised mechanisms in our biologically-inspired, socially-adaptive agent model, and conduct our investigation in a small society of artificial agents whose goal is to survive in challenging environments. Our results find that, while stress can be adaptive and regulated through affective social support, long-term behavioural and physiological adaptation is determined by the contextual perception of affective social bonds, which is influenced by early-stage interactions between affective social bond partners as well as the degree of the physical and social challenges. We also show how these low-level effects associated with oxytocin and cortisol can be used as “biomarkers” of social support and environmental stress. For socially-situated artificial agents, we suggest that these “social buffering” mechanisms can adapt the (adaptive) stress mechanisms, but that the long-term efficacy of this adaptation is related to the temporal dynamics of social interactions and the contextual perception of the affective social and physical environments.Peer reviewe

Musical Contour Regulation Facilitation (MCRF) to Support Emotion Regulation Development in Preschoolers: A Mixed Methods Feasibility Study

Title from PDF of title page, viewed on July 7, 2015Dissertation advisor: William EverettVitaIncludes bibliographic references (pages 216-236)Thesis (Ph.D.)--Conservatory of Music and Dance and School of Education. University of Missouri--Kansas City, 2015Emotion regulation (ER) is the ability for a person to maintain a comfortable state of arousal by controlling and shifting his or her emotional experiences and expressions. The emergence of maladaptive ER occurs in childhood and is one characteristic often shared by several disorders. Maladaptive ER can significantly affect multiple areas in child development, such as the ability to learn in school, form and maintain healthy relationships with peers and adults, and manage and inhibit behavioral responses. Interventions for children at-risk for developing maladaptive ER skills are limited and need further exploration. Based on limitations noted in existing treatment options, this study provided a preliminary examination of the utility of using a music-based approach. An embedded convergent mixed methods research design was used to explore the feasibility of a Musical Contour Regulation Facilitation (MCRF) intervention. The MCRF intervention was developed to improve ER abilities in children by providing opportunities to practice real-time management of high and low arousal experiences. Typically developing preschool-aged children (n = 8) participated in 11 MCRF sessions over four weeks. Data to assess ER skills and related behaviors was collected pre- and post-MCRF treatment; current regulatory levels were assessed and self-reported at the beginning and end of each MCRF session. In addition, parent and teacher interviews and questionnaires were conducted post-treatment. Grounded theory-based qualitative analysis results suggest that most parents and both teachers noted emotional changes in the children following MCRF treatment. Perhaps more importantly, all interviewees believed in the importance and helpfulness of music on developmental outcomes even if they did not note changes in the children or they recognized that other factors may have contributed to perceived changes. Quantitative data analysis results indicated clinically significant improvements in ER skills in the children following MCRF treatment. Convergent mixed methods analyses results further support the efficacy and acceptability of the MCRF intervention. Together, these findings endorse future normative and clinical study of the MCRF intervention as way to facilitate ER development, especially as this medium is highly desired by parents and teachers and can be easily integrated in a preschool setting.Introduction -- Emotion regulation and musical contour regulation facilitation in theory and practice: an integrated literature review -- The effectiveness of MCRF in facilitating emotion regulation: methodology for a feasibility study -- The effectiveness of MCRF in facilitating emotion regulation: mixed methods results -- The feasibility of MCRF in facilitating emotion regulation development in preschoolers: discussion, implications, and recommendations -- Appendix A. Musical contour regulation facilitation (MCRF) intervention manual -- Appendix B. MCRF intervention pilot assessment -- Appendix C. Recruitment materials -- Appendix D. Informed consent and child assent -- Appendix E. Study measure