A cognitive architecture for inner speech

A cognitive architecture for inner speech is presented. It is based on the Standard Model of Mind, integrated with modules for self- talking. Briefly, the working memory of the proposed architecture includes the phonological loop as a component which manages the exchanging information between the phonological store and the articulatory control system. The inner dialogue is modeled as a loop where the phonological store hears the inner voice produced by the hidden articulator process. A central executive module drives the whole system, and contributes to the generation of conscious thoughts by retrieving information from long-term memory. The surface form of thoughts thus emerges by the phonological loop. Once a conscious thought is elicited by inner speech, the perception of new context takes place and then repeating the cognitive loop. A preliminary formalization of some of the described processes by event cal- culus, and early results of their implementation on the humanoid robot Pepper by SoftBank Robotics are discussed

A calculus for robot inner speech and self-awareness

The inner speech is the common mental experience the humans have when they dialogue with themselves. It is widely acknowledged that inner speech is related to awareness and self-awareness. The inner speech reproduces and expands in the mind social and physical sources of awareness. In this preliminary work, a calculus based on a first-order modal logic to automate inner speech is presented. It attempts to make the existing inner speech theories suitable for robot. By making robot able to talk to itself, it is possible to analyze the role of inner speech in robot awareness and self-awareness, opening new interesting research scenarios not yet investigated

Visually-Grounded Language Model for Human-Robot Interaction

Visually grounded human-robot interaction is recognized to be an essential ingredient of socially intelligent robots, and the integration of vision and language increasingly attracts attention of researchers in diverse fields. However, most systems lack the capability to adapt and expand themselves beyond the preprogrammed set of communicative behaviors. Their linguistic capabilities are still far from being satisfactory which make them unsuitable for real-world applications. In this paper we will present a system in which a robotic agent can learn a grounded language model by actively interacting with a human user. The model is grounded in the sense that meaning of the words is linked to a concrete sensorimotor experience of the agent, and linguistic rules are automatically extracted from the interaction data. The system has been tested on the NAO humanoid robot and it has been used to understand and generate appropriate natural language descriptions of real objects. The system is also capable of conducting a verbal interaction with a human partner in potentially ambiguous situations

Categories, Quantum Computing, and Swarm Robotics: A Case Study

The swarms of robots are examples of artificial collective intelligence, with simple individual autonomous behavior and emerging swarm effect to accomplish even complex tasks. Modeling approaches for robotic swarm development is one of the main challenges in this field of research. Here, we present a robot-instantiated theoretical framework and a quantitative worked-out example. Aiming to build up a general model, we first sketch a diagrammatic classification of swarms relating ideal swarms to existing implementations, inspired by category theory. Then, we propose a matrix representation to relate local and global behaviors in a swarm, with diagonal sub-matrices describing individual features and off-diagonal sub-matrices as pairwise interaction terms. Thus, we attempt to shape the structure of such an interaction term, using language and tools of quantum computing for a quantitative simulation of a toy model. We choose quantum computing because of its computational efficiency. This case study can shed light on potentialities of quantum computing in the realm of swarm robotics, leaving room for progressive enrichment and refinement

Developing Self-Awareness in Robots via Inner Speech

The experience of inner speech is a common one. Such a dialogue accompanies the introspection of mental life and fulfills essential roles in human behavior, such as self-restructuring, self-regulation, and re-focusing on attentional resources. Although the underpinning of inner speech is mostly investigated in psychological and philosophical fields, the research in robotics generally does not address such a form of self-aware behavior. Existing models of inner speech inspire computational tools to provide a robot with this form of self-awareness. Here, the widespread psychological models of inner speech are reviewed, and a cognitive architecture for a robot implementing such a capability is outlined in a simplified setup

Robots as intelligent assistants to face COVID-19 pandemic

Motivation: The epidemic at the beginning of this year, due to a new virus in the coronavirus family, is causing many deaths and is bringing the world economy to its knees. Moreover, situations of this kind are historically cyclical. The symptoms and treatment of infected patients are, for better or worse even for new viruses, always the same: More or less severe flu symptoms, isolation and full hygiene. By now man has learned how to manage epidemic situations, but deaths and negative effects continue to occur. What about technology? What effect has the actual technological progress we have achieved? In this review, we wonder about the role of robotics in the fight against COVID. It presents the analysis of scientific articles, industrial initiatives and project calls for applications from March to now highlighting how much robotics was ready to face this situation, what is expected from robots and what remains to do. Results: The analysis was made by focusing on what research groups offer as a means of support for therapies and prevention actions. We then reported some remarks on what we think is the state of maturity of robotics in dealing with situations like COVID-19

Robot’s Inner Speech Effects on Human Trust and Anthropomorphism

Inner Speech is an essential but also elusive human psychological process that refers to an everyday covert internal conversation with oneself. We argued that programming a robot with an overt self-talk system that simulates human inner speech could enhance both human trust and users’ perception of robot’s anthropomorphism, animacy, likeability, intelligence and safety. For this reason, we planned a pre-test/post-test control group design. Participants were divided in two different groups, one experimental group and one control group. Participants in the experimental group interacted with the robot Pepper equipped with an over inner speech system whereas participants in the control group interacted with the robot that produces only outer speech. Before and after the interaction, both groups of participants were requested to complete some questionnaires about inner speech and trust. Results showed differences between participants’ pretest and post-test assessment responses, suggesting that the robot’s inner speech influences in participants of experimental group the perceptions of animacy and intelligence in robot. Implications for these results are discussed

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator

Correlation of diagnostic efficacy of unhealthy cervix by cytology, colposcopy and histopathology in women of rural areas

Background: The objective was to assess the sensitivity and specificity of pap smear and colposcopy and to study the socio demographic parameters of women with unhealthy cervix. Methods: This was a prospective observational study conducted from August 2011 – August 2013 in the Department of Obstetrics and Gynaecology. Pap smear was performed by the conventional method and colposcopy was done for all 200 sexually active women who came with complaints of discharge per vagina, inter menstrual or post coital bleeding. Colposcopy results were analysed. Final correlation of pap smear and colposcopy were based on histopathology.Results: There were 200 samples that were suitable for statistical analysis. The sensitivity of colposcopy was 79.37%, specificity 81.02%, positive predictive value 65.79%, negative predictive value 89.52% respectively and accuracy was 80.5%. Pap smear had a sensitivity of 25.4%, specificity of 99.27%, positive predictive value of 94.12%, negative predictive value of 74.32%, and accuracy of 76.0% respectively.Conclusions: Pap smear had a poorer sensitivity compared to Colposcopy but a better specificity than colposcopy. Hence it may be better to utilise both tests as they complement each other in screening of premalignant lesions of cervix

A global workspace theory model for trust estimation in human-robot interaction

Successful and genuine social connections between humans are based on trust, even more when the people involved have to collaborate to reach a shared goal. With the advent of new findings and technologies in the field of robotics, it appears that this same key factor that regulates relationships between humans also applies with the same importance to human-robot interactions (HRI). Previous studies have proven the usefulness of a robot able to estimate the trustworthiness of its human collaborators and in this position paper we discuss a method to extend an existing state-of-the-art trust model with considerations based on social cues such as emotions. The proposed model follows the Global Workspace Theory (GWT) principles to build a novel system able to combine multiple specialised expert systems to determine whether the partner can be considered trustworthy or not. Positive results would demonstrate the usefulness of using constructive biases to enhance the teaming skills of social robots