Social Cognition for Human-Robot Symbiosis-Challenges and Building Blocks

You Were Always on My Mind: Introducing Chef's Hat and COPPER for Personalized Reinforcement Learning

Reinforcement learning simulation environments pose an important experimental test bed and facilitate data collection for developing AI-based robot applications. Most of them, however, focus on single-agent tasks, which limits their application to the development of social agents. This study proposes the Chef's Hat simulation environment, which implements a multi-agent competitive card game that is a complete reproduction of the homonymous board game, designed to provoke competitive strategies in humans and emotional responses. The game was shown to be ideal for developing personalized reinforcement learning, in an online learning closed-loop scenario, as its state representation is extremely dynamic and directly related to each of the opponent's actions. To adapt current reinforcement learning agents to this scenario, we also developed the COmPetitive Prioritized Experience Replay (COPPER) algorithm. With the help of COPPER and the Chef's Hat simulation environment, we evaluated the following: (1) 12 experimental learning agents, trained via four different regimens (self-play, play against a naive baseline, PER, or COPPER) with three algorithms based on different state-of-the-art learning paradigms (PPO, DQN, and ACER), and two "dummy" baseline agents that take random actions, (2) the performance difference between COPPER and PER agents trained using the PPO algorithm and playing against different agents (PPO, DQN, and ACER) or all DQN agents, and (3) human performance when playing against two different collections of agents. Our experiments demonstrate that COPPER helps agents learn to adapt to different types of opponents, improving the performance when compared to off-line learning models. An additional contribution of the study is the formalization of the Chef's Hat competitive game and the implementation of the Chef's Hat Player Club, a collection of trained and assessed agents as an enabler for embedding human competitive strategies in social continual and competitive reinforcement learning.

Machines Like Us and People Like You: Toward Human-Robot Shared Experience

In the past years, the field of collaborative robots has been developing fast, with applications ranging from health care to search and rescue, construction, entertainment, sports, and many others. However, current social robotics is still far from the general abilities we expect in a robot collaborator. This limitation is more evident when robots are faced with real-life contexts and activities occurring over long periods. In this article, we argue that human-robot collaboration is more than just being able to work side by side on complementary tasks: collaboration is a complex relational process that entails mutual understanding and reciprocal adaptation. Drawing on this assumption, we propose to shift the focus from "human-robot interaction" to "human-robot shared experience." We hold that for enabling the emergence of such shared experiential space between humans and robots, constructs such as coadaptation, intersubjectivity, individual differences, and identity should become the central focus of modeling. Finally, we suggest that this shift in perspective would imply changing current mainstream design approaches, which are mainly focused on functional aspects of the human-robot interaction, to the development of architectural frameworks that integrate the enabling dimensions of social cognition.

Man is a "Rope" Stretched Between Virosphere and Humanoid Robots: On the Urgent Need of an Ethical Code for Ecosystem Survival

In this paper we compare the strategies applied by two successful biological components of the ecosystem, the viruses and the human beings, to interact with the environment. Viruses have had and still exert deep and vast actions on the ecosystem especially at the genome level of most of its biotic components. We discuss on the importance of the human being as contraptions maker in particular of robots, hence of machines capable of automatically carrying out complex series of actions. Beside the relevance of designing and assembling these contraptions, it is of basic importance the goal for which they are assembled and future scenarios of their possible impact on the ecosystem. We can't procrastinate the development and implementation of a highly inspired and stringent "ethical code" for human beings and humanoid robots because it will be a crucial aspect for the wellbeing of the mankind and of the entire ecosystem.

Copresence With Virtual Humans in Mixed Reality: The Impact of Contextual Responsiveness on Social Perceptions

Virtual humans (VHs)—automated, three-dimensional agents—can serve as realistic embodiments for social interactions with human users. Extant literature suggests that a user’s cognitive and affective responses toward a VH depend on the extent to which the interaction elicits a sense of copresence, or the subjective “sense of being together.” Furthermore, prior research has linked copresence to important social outcomes (e.g., likeability and trust), emphasizing the need to understand which factors contribute to this psychological state. Although there is some understanding of the determinants of copresence in virtual reality (VR) (cf. Oh et al., 2018), it is less known what determines copresence in mixed reality (MR), a modality wherein VHs have unique access to social cues in a “real-world” setting. In the current study, we examined the extent to which a VH’s responsiveness to events occurring in the user’s physical environment increased a sense of copresence and heightened affective connections to the VH. Participants (N = 65) engaged in two collaborative tasks with a (nonspeaking) VH using an MR headset. In the first task, no event in the participant’s physical environment would occur, which served as the control condition. In the second task, an event in the participants’ physical environment occurred, to which the VH either responded or ignored depending on the experimental condition. Copresence and interpersonal evaluations of the VHs were measured after each collaborative task via self-reported measures. Results show that when the VH responded to the physical event, participants experienced a significant stronger sense of copresence than when the VH did not respond. However, responsiveness did not elicit more positive evaluations toward the VH (likeability and emotional connectedness). This study is an integral first step in establishing how and when affective and cognitive components of evaluations during social interactions diverge. Importantly, the findings suggest that feeling copresence with VH in MR is partially determined by the VHs’ response to events in the actual physical environment shared by both interactants.

Social Robots for Supporting Post-traumatic Stress Disorder Diagnosis and Treatment

Post-Traumatic Stress Disorder (PTSD) is a severe psychiatric disorder with profound public health impact due to its high prevalence, chronic nature, accompanying functional impairment, and frequently occurring comorbidities. Early PTSD symptoms, often observed shortly after trauma exposure, abate with time in the majority of those who initially express them, yet leave a significant minority with chronic PTSD. While the past several decades of PTSD research have produced substantial knowledge regarding the mechanisms and consequences of this debilitating disorder, the diagnosis of and available treatments for PTSD still face significant challenges. Here, we discuss how novel therapeutic interventions involving social robots can potentially offer meaningful opportunities for overcoming some of the present challenges. As the application of social robotics-based interventions in the treatment of mental disorders is only in its infancy, it is vital that careful, well-controlled research is conducted to evaluate their efficacy, safety, and ethics. Nevertheless, we are hopeful that robotics-based solutions could advance the quality, availability, specificity and scalability of care for PTSD.

Homology modeling in the time of collective and artificial intelligence

Homology modeling is a method for building protein 3D structures using protein primary sequence and utilizing prior knowledge gained from structural similarities with other proteins. The homology modeling process is done in sequential steps where sequence/structure alignment is optimized, then a backbone is built and later, side-chains are added. Once the low-homology loops are modeled, the whole 3D structure is optimized and validated. In the past three decades, a few collective and collaborative initiatives allowed for continuous progress in both homology and ab initio modeling. Critical Assessment of protein Structure Prediction (CASP) is a worldwide community experiment that has historically recorded the progress in this field. Folding@Home and Rosetta@Home are examples of crowd-sourcing initiatives where the community is sharing computational resources, whereas RosettaCommons is an example of an initiative where a community is sharing a codebase for the development of computational algorithms. Foldit is another initiative where participants compete with each other in a protein folding video game to predict 3D structure. In the past few years, contact maps deep machine learning was introduced to the 3D structure prediction process, adding more information and increasing the accuracy of models significantly. In this review, we will take the reader in a journey of exploration from the beginnings to the most recent turnabouts, which have revolutionized the field of homology modeling. Moreover, we discuss the new trends emerging in this rapidly growing field.

Homecare Robotic Systems for Healthcare 4.0: Visions and Enabling Technologies

Powered by the technologies that have originated from manufacturing, the fourth revolution of healthcare technologies is happening (Healthcare 4.0). As an example of such revolution, new generation homecare robotic systems (HRS) based on the cyber-physical systems (CPS) with higher speed and more intelligent execution are emerging. In this article, the new visions and features of the CPS-based HRS are proposed. The latest progress in related enabling technologies is reviewed, including artificial intelligence, sensing fundamentals, materials and machines, cloud computing and communication, as well as motion capture and mapping. Finally, the future perspectives of the CPS-based HRS and the technical challenges faced in each technical area are discussed.

Fusing autonomy and sociality via embodied emergence and development of behaviour and cognition from fetal period

Human-centred AI/Robotics are quickly becoming important. Their core claim is that AI systems or robots must be designed and work for the benefits of humans with no harm or uneasiness. It essentially requires the realization of autonomy, sociality and their fusion at all levels of system organization, even beyond programming or pre-training. The biologically inspired core principle of such a system is described as the emergence and development of embodied behaviour and cognition. The importance of embodiment, emergence and continuous autonomous development is explained in the context of developmental robotics and dynamical systems view of human development. We present a hypothetical early developmental scenario that fills in the very beginning part of the comprehensive scenarios proposed in developmental robotics. Then our model and experiments on emergent embodied behaviour are presented. They consist of chaotic maps embedded in sensory–motor loops and coupled via embodiment. Behaviours that are consistent with embodiment and adaptive to environmental structure emerge within a few seconds without any external reward or learning. Next, our model and experiments on human fetal development are presented. A precise musculo-skeletal fetal body model is placed in a uterus model. Driven by spinal nonlinear oscillator circuits coupled together via embodiment, somatosensory signals are evoked and learned by a model of the cerebral cortex with 2.6 million neurons and 5.3 billion synapses. The model acquired cortical representations of self–body and multi-modal sensory integration. This work is important because it models very early autonomous development in realistic detailed human embodiment. Finally, discussions toward human-like cognition are presented including other important factors such as motivation, emotion, internal organs and genetic factors.

This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

Perspectives and Challenges in Robotic Neurorehabilitation

The development of robotic devices for rehabilitation is a fast-growing field. Nowadays, thanks to novel technologies that have improved robots’ capabilities and offered more cost-effective solutions, robotic devices are increasingly being employed during clinical practice, with the goal of boosting patients’ recovery. Robotic rehabilitation is also widely used in the context of neurological disorders, where it is often provided in a variety of different fashions, depending on the specific function to be restored. Indeed, the effect of robot-aided neurorehabilitation can be maximized when used in combination with a proper training regimen (based on motor control paradigms) or with non-invasive brain machine interfaces. Therapy-induced changes in neural activity and behavioral performance, which may suggest underlying changes in neural plasticity, can be quantified by multimodal assessments of both sensorimotor performance and brain/muscular activity pre/post or during intervention. Here, we provide an overview of the most common robotic devices for upper and lower limb rehabilitation and we describe the aforementioned neurorehabilitation scenarios. We also review assessment techniques for the evaluation of robotic therapy. Additional exploitation of these research areas will highlight the crucial contribution of rehabilitation robotics for promoting recovery and answering questions about reorganization of brain functions in response to disease.