Bioinspired decision-making for a socially interactive robot

Nowadays, robots and humans coexist in real settings where robots need to interact autonomously making their own decisions. Many applications require that robots adapt their behavior to different users and remember each user’s preferences to engage them in the interaction. To this end, we propose a decision making system for social robots that drives their actions taking into account the user and the robot’s state. This system is based on bio-inspired concepts, such as motivations, drives and wellbeing, that facilitate the rise of natural behaviors to ease the acceptance of the robot by the users. The system has been designed to promote the human-robot interaction by using drives and motivations related with social aspects, such as the users’ satisfaction or the need of social interaction. Furthermore, the changes of state produced by the users’ exogenous actions have been modeled as transitional states that are considered when the next robot’s action has to be selected. Our system has been evaluated considering two different user profiles. In the proposed system, user’s preferences are considered and alter the homeostatic process that controls the decision making system. As a result, using reinforcement learning algorithms and considering the robot’s wellbeing as the reward function, the social robot Mini has learned from scratch two different policies of action, one for each user, that fit the users’ preferences. The robot learned behaviors that maximize its wellbeing as well as keep the users engaged in the interactions.The research leading to these results has received funding from the projects: Development of social robots to help seniors with cognitive impairment (ROBSEN), funded by the Ministerio de Economía y Competitividad (DPI2014-57684-R); and RoboCity2030-III-CM, funded by Comunidad de Madrid and cofunded by Structural Funds of the EU (S2013/MIT-2748).Publicad

Abel: Integrating Humanoid Body, Emotions, and Time Perception to Investigate Social Interaction and Human Cognition

Humanoids have been created for assisting or replacing humans in many applications, providing encouraging results in contexts where social and emotional interaction is required, such as healthcare, education, and therapy. Bioinspiration, that has often guided the design of their bodies and minds, made them also become excellent research tools, probably the best platform by which we can model, test, and understand the human mind and behavior. Driven by the aim of creating a believable robot for interactive applications, as well as a research platform for investigating human cognition and emotion, we are constructing a new humanoid social robot: Abel. In this paper, we discussed three of the fundamental principles that motivated the design of Abel and its cognitive and emotional system: hyper-realistic humanoid aesthetics, human-inspired emotion processing, and human-like perception of time. After reporting a brief state-of-the-art on the related topics, we present the robot at its stage of development, what are the perspectives for its application, and how it could satisfy the expectations as a tool to investigate the human mind, behavior, and consciousness

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

Robot-Locust Social Information Transfer Occurs in Predator Avoidance Contexts

Social learning is an evolutionarily important ability increasingly attributed also to invertebrate species. Interfacing robots with animals represents a promising strategy to investigate social learning. Herein, we studied if the gregarious form of Locusta migratoria, a particularly suited model to examine social learning, can use social information provided by robotic demonstrators to optimize their predator avoidance. Robotic demonstrators with different silhouettes and colours (biomimetic or neutral) were used to investigate if their rotation on a rod (e.g. hiding behaviour) elicited the same behaviour in neighbouring locusts. Locusts’ responses were affected by different robotic demonstrators, observing a significant impact of the biomimetic silhouette in reducing the latency duration, and in promoting social learning (e.g. locusts displaying hiding behaviour after observing it in robotic demonstrators). A significant impact of colour patterns in triggering socially induced hiding behaviour was also recorded, especially when the biomimetic silhouette was coloured with the gregarious-like pattern. This research indicates gregarious locusts exploit social information in specific ecological contexts, providing basic knowledge on the complex behavioural ecology and social biology in invertebrates. The proposed animal-robot interaction paradigm shows the role of robots as carrier of social information to living organisms, suggesting social biorobotics as advanced and sustainable approach for socio-biology investigation, and environmental management

Cognitive system framework for brain-training exercise based on human-robot interactif

“This is a post-peer-review, pre-copyedit version of an article published inCognitive computation. The final authenticated version is available online at: http://dx.doi.org/10.1007/s12559-019-09696-2Every 3 seconds, someone develops dementia worldwide. Brain-training exercises, preferably involving also physical activity, have shown their potential to monitor and improve the brain function of people affected by Alzheimer disease (AD) or mild cognitive impairment (MCI). This paper presents a cognitive robotic system designed to assist mild dementia patients during brain-training sessions of sorting tokens, an exercise inspired by the Syndrom KurzTest neuropsychological test (SKT). The system is able to perceive, learn and adapt to the user’s behaviour and is composed of two main modules. The adaptive module based on representing the human-robot interaction as a planning problem, that can adapt to the user performance offering different encouragement and recommendation actions using both verbal and gesture communication in order to minimize the time spent to solve the exercise. As safety is a very important issue, the cognitive system is enriched with a safety module that monitors the possibility of physical contact and reacts accordingly. The cognitive system is presented as well as its embodiment in a real robot. Simulated experiments are performed to (i) evaluate the adaptability of the system to different patient use-cases and (ii) validate the coherence of the proposed safety module. A real experiment in the lab, with able users, is used as preliminary evaluation to validate the overall approach. Results in laboratory conditions show that the two presented modules effectively provide additional and essential functionalities to the system, although further work is necessary to guarantee robustness and timely response of the robot before testing it with patientsPeer ReviewedPostprint (author's final draft

Reinforcement Learning Approaches in Social Robotics

This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field

Using robots to understand animal cognition

In recent years, robotic animals and humans have been used to answer a variety of questions related to behavior. In the case of animal behavior, these efforts have largely been in the field of behavioral ecology. They have proved to be a useful tool for this enterprise as they allow the presentation of naturalistic social stimuli whilst providing the experimenter with full control of the stimulus. In interactive experiments, the behavior of robots can be controlled in a manner that is impossible with real animals, making them ideal instruments for the study of social stimuli in animals. This paper provides an overview of the current state of the field and considers the impact that the use of robots could have on fundamental questions related to comparative psychology: namely, perception, spatial cognition, social cognition, and early cognitive development. We make the case that the use of robots to investigate these key areas could have an important impact on the field of animal cognition

Exploring the intersection of biology and design for product innovations

Design, development, productization, and applications of advanced product concepts are pressing for higher multifunctionality, resilience, and maximization of available resources equitably to meet the growing and continuing demands of global customers. These demands have further accelerated during the recent COVID- 19 pandemic and are continuing to be a challenge. Engineering designs are one of the most effective ways to endow products with functions, resilience, and sustainability. Biology, through millions of years of evolution, has met these acute requirements under severe resource and environmental constraints. As the manufacturing of products is reaching the fundamental limits of raw materials, labor, and resource constraints in terms of availability, accessibility, and affordability, new approaches are a call to action to meet these challenges. Understanding the designs in biology is an attractive, novel, and desired frontier for learning and implementation to meet this call to action. This is the focus of the paper discussed through examples for convergence of fundamental engineering design concepts and the lessons learned and applied from biology

Collective responses of a large mackerel school depend on the size and speed of a robotic fish but not on tail motion

So far, actuated fish models have been used to study animal interactions in small-scale controlled experiments. This study, conducted in a semi-controlled setting, investigates robot5interactions with a large wild-caught marine fish school (∼3000 individuals) in their natural social environment. Two towed fish robots were used to decouple size, tail motion and speed in a series of sea-cage experiments. Using high-resolution imaging sonar and sonar-video blind scoring, we monitored and classified the school's collective reaction towards the fish robots as attraction or avoidance. We found that two key releasers—the size and the speed of the robotic fish—were responsible for triggering either evasive reactions or following responses. At the same time, we found fish reactions to the tail motion to be insignificant. The fish evaded a fast-moving robot even if it was small. However, mackerels following propensity was greater towards a slow small robot. When moving slowly, the larger robot triggered significantly more avoidance responses than a small robot. Our results suggest that the collective responses of a large school exposed to a robotic fish could be manipulated by tuning two principal releasers—size and speed. These results can help to design experimental methods for in situ observations of wild fish schools or to develop underwater robots for guiding and interacting with free-ranging aggregated aquatic organisms.This work was financed by the Norwegian Research Council (grant 204229/F20) and Estonian Government Target Financing (grant SF0140018s12). JCC was partially supported by a grant from Iceland, Liechtenstein and Norway through the EEA Financial Mechanism, operated by Universidad Complutense de Madrid. We are grateful to A. Totland for his technical help. The animal collection was approved by The Royal Norwegian Ministry of Fisheries, and the experiment was approved by the Norwegian Animal Research Authority. The Institute of Marine Research is permitted to conduct experiments at the Austevoll aquaculture facility by the Norwegian Biological Resource Committee and the Norwegian Animal Research Committee (Forsøksdyrutvalget)