Companion Robots Research Articles

The Tactile Ethics of Soft Robotics: Designing Wisely for Human-Robot Interaction.

Soft robots promise an exciting design trajectory in the field of robotics and human-robot interaction (HRI), promising more adaptive, resilient movement within environments as well as a safer, more sensitive interface for the objects or agents the robot encounters. In particular, tactile HRI is a critical dimension for designers to consider, especially given the onrush of assistive and companion robots into our society. In this article, we propose to surface an important set of ethical challenges for the field of soft robotics to meet. Tactile HRI strongly suggests that soft-bodied robots balance tactile engagement against emotional manipulation, model intimacy on the bonding with a tool not with a person, and deflect users from personally and socially destructive behavior the soft bodies and surfaces could normally entice.

Variable structure robot control systems: The RAPP approach

This paper presents a method of designing variable structure control systems for robots. As the on-board robot computational resources are limited, but in some cases the demands imposed on the robot by the user are virtually limitless, the solution is to produce a variable structure system. The task dependent part has to be exchanged, however the task governs the activities of the robot. Thus not only exchange of some task-dependent modules is required, but also supervisory responsibilities have to be switched. Such control systems are necessary in the case of robot companions, where the owner of the robot may demand from it to provide many services.

Domestic robots: Has their time finally come?

PurposeThis paper aims to provide details of commercially available domestic robots and recent product developments and consider whether a significant boost in the robot population is imminent.Design/methodology/approachFollowing a short introduction, this paper first provides a brief overview of existing domestic robots and identifies recent product trends. It then discusses some newer product developments which extend the capabilities of domestic robots. This is followed by a consideration of the many recently launched companion robots, and the paper concludes with a discussion of the likely impact on the domestic robot market.FindingsThis paper shows that domestic robots have benefited from developments in artificial intelligence, sensor technology and connectivity, which have led to greater versatility and enhanced ease of use. Several new product developments are extending the range of functions conducted by domestic robots. Many small, mobile companion/social robots have recently been developed which interact with humans by speech and vision and conduct functions such as entertainment, the control of household appliances and security.Originality/valueThis paper provides an insight into the wide range of domestic robots which are available or under development and considers their commercial prospects.

Shopping with a robotic companion

In this paper, we present a robotic shopping assistant, designed with a cognitive architecture, grounded in machine learning systems, in order to study how the human-robot interaction (HRI) is changing the shopping behavior in smart technological stores. In the software environment of the NAO robot, connected to the Internet with cloud services, we designed a social-like interaction where the robot carries out actions with the customer. In particular, we focused our design on two main skills the robot has to learn: the first is the ability to acquire social input communicated by relevant clues that humans provide about their emotional state (emotions, emotional speech), or collected in the Social Media (such as, information on the customer's tastes, cultural background, etc.). The second is the skill to express in turn its own emotional state, so that it can affect the customer buying decision, refining in the user the sense of interacting with a human-like companion. By combining social robotics and machine learning systems the potential of robotics to assist people in real life situations will increase, providing a gentle customers' acceptance of advanced technologies.

Roboty społeczne i praca socjalna

The goal of this article is to seek a path of cooperation between engineers and sociologists in view of the construction of increasingly sophisticated social robots. First, I show how humanoid and animaloid companion robots are used in the field of applied sociology and social work. Secondly, I explore several problems relating to social robotics, and in particular those of effectiveness and safety of human-robot interaction. Finally, I emphasize that two key concepts in this research area are scenario and persona. Trained sociologists and psychologists may give a substantive contribution to the development of social robotics by helping engineers in the creation of scenarios and personas.

Interacting with Dementia: The MARIO Approach.

MARIO is a companion robot that aims to help people with dementia (PWD) to battle isolation and loneliness by enabling them to stay socially active by providing a number of applications focused on hobbies (music, movies, etc), staying engaged with communities (reading headlines, reading local twitter feeds etc.) and staying connected with family and friends (telephoning them, reading their news from twitter, etc.). This paper presents the results from the initial trials of MARIO interacting with PWD involving a limited set of applications. It confirms some of the challenges hypothesized at the outset of the study and provides guidelines for future development work.

Parallel Interacting Multiple Model-Based Human Motion Prediction for Motion Planning of Companion Robots

We propose in this paper an autonomous motion planning framework for companion robots to accompany humans in a socially desirable manner, which takes safety and comfort requirements into account. The overall framework consists of two parts: first, a novel parallel interacting multiple model-unscented Kalman filter (PIMM-UKF) approach is developed to simultaneously estimate human motion states and model mismatch, and then systematically predict the position and velocity of the human for a finite horizon. Second, based on the predicted human states, a nonlinear model predictive control (MPC) technique is utilized for the robot motion planning. The simulation results have demonstrated the superior performance in prediction using the PIMM-UKF approach. The effectiveness of the MPC planner is also shown by successfully facilitating the socially desirable companion behavior.

Et Tu, Android? Regulating Dangerous and Dishonest Robots

Consumer robots like personal digital assistants, automated cars, robot companions, chore-bots, and personal drones raise common consumer protection issues, such as fraud, privacy, data security, and risks to health, physical safety, and finances. They also raise new consumer protection issues, or at least call into question how existing consumer protection regimes might be applied to such emerging technologies. Yet it is unclear which legal regimes should govern these robots and what consumer protection rules for robots should look like.This paper argues that the FTC's grant of authority and existing jurisprudence are well-suited for protecting consumers who buy and interact with robots. The FTC has proven to be a capable regulator of communications, organizational procedures, and design, which are the three crucial concepts for safe consumer robots.

Care staff perceptions of a social robot called Paro and a look-alike Plush Toy: a descriptive qualitative approach

ABSTRACTObjectives: Social robots such as Paro, a therapeutic companion robot, have recently been introduced into dementia care as a means to reduce behavioural and psychological symptoms of dementia. The purpose of this study was to explore care staff perceptions of Paro and a look-alike non-robotic animal, including benefits and limitations in dementia care.Methods: The study assumed a descriptive qualitative approach, nested within a large cluster-randomised controlled trial. We interviewed a subsample of 20 facility care staff, from nine long-term care facilities in Southeast Queensland, Australia. Thematic analysis of the data, which was inductive and data-driven, was undertaken with the assistance of the qualitative software, ATLAS.ti®.Results: The findings refer to four categories: increasing excitement for Paro and decreasing enthusiasm for Plush Toy; value and function of Paro; opportunities for engagement; and alternatives vs. robustness.Conclusion: Staff caring for people with dementia preferred Paro compared to a look-alike Plush Toy. Staff identified that Paro had the potential to improve quality of life for people with dementia, whereas the Plush Toy had limitations when compared to Paro. However, participants expressed concern that the cost of Paro could reduce opportunities for use within aged care.

Nudging for good: robots and the ethical appropriateness of nurturing empathy and charitable behavior

An under-examined aspect of human---robot interaction that warrants further exploration is whether robots should be permitted to influence a user's behavior for that person's own good. Yet an even more controversial practice could be on the horizon, which is allowing a robot to "nudge" a user's behavior for the good of society. In this article, we examine the feasibility of creating companion robots that would seek to nurture a user's empathy toward other human beings. As more and more computing devices subtly and overtly influence human behavior, it is important to draw attention to whether it would be ethically appropriate for roboticists to pursue this type of design pathway. Our primary focus is on whether a companion robot could encourage humans to perform charitable acts; this design possibility illustrates the range of socially just actions that a robot could potentially elicit from a user and what the associated ethical concerns may be.

Living with the animals: animal or robotic companions for the elderly in smart homes?

Although the use of pet robots in senior living facilities and day-care centres, particularly for individuals suffering from dementia, has been intensively researched, the question of introducing pet robots into...

Intelligent emotion and behavior based on topological consciousness and adaptive resonance theory in a companion robot

Abstract Companion or ‘pet’ robots can be expected to be an important part of a future in which robots contribute to our lives in many ways. An understanding of emotional interactions would be essential to such robots’ behavior. To improve the cognitive and behavior systems of such robots, we propose the use of an artificial topological consciousness that uses a synthetic neurotransmitter and motivation, including a biologically inspired emotion system. A fundamental aspect of a companion robot is a cross-communication system that enables natural interactions between humans and the robot. This paper focuses on three points in the development of our proposed framework: (1) the organization of the behavior including inside-state emotion regarding the phylogenetic consciousness-based architecture; (2) a method whereby the robot can have empathy toward its human user’s expressions of emotion; and (3) a method that enables the robot to select a facial expression in response to the human user, providing instant human-like ‘emotion’ and based on emotional intelligence (EI) that uses a biologically inspired topological online method to express, for example, encouragement or being delighted. We also demonstrate the performance of the artificial consciousness based on the complexity level and a robot’s social expressions that are designed to enhance the users affinity with the robot.

A Situation-Aware Fear Learning (SAFEL) model for robots

This work proposes a novel Situation-Aware FEar Learning (SAFEL) model for robots. SAFEL combines concepts of situation-aware expert systems with well-known neuroscientific findings on the brain fear-learning mechanism to allow companion robots to predict undesirable or threatening situations based on past experiences. One of the main objectives is to allow robots to learn complex temporal patterns of sensed environmental stimuli and create a representation of these patterns. This memory can be later associated with a negative or positive “emotion”, analogous to fear and confidence. Experiments with a real robot demonstrated SAFEL's success in generating contextual fear conditioning behavior with predictive capabilities based on situational information.

Cheery companions or serious assistants? Role and demeanor congruity as predictors of robot attraction and use intentions among senior citizens

Using a robot designed for senior citizens in a retirement home setting, an experiment (N=51) was conducted to investigate whether variations in the role (companion vs. assistant) and social demeanor (playful vs. serious) of a robot influence senior citizens’ perceptions of the robot's social attractiveness, intelligence, anxiety, and eeriness. Results show that assistant robots are perceived as more socially attractive and intelligent when their demeanor is playful rather than serious. In addition, companion robots are evaluated as less anxious and less eerie when their personality is serious rather than playful. Finally, companion robots with a serious demeanor have a positive indirect effect on robot use intentions via heightened perceptions of intelligence and social attractiveness, while assistant robots with a playful demeanor have a positive indirect effect on intentions via lower perceptions of anxiety. The implications of our findings for human-robot interaction and the design of socially assistive robotics are discussed.

Motion control for a walking companion robot with a novel human–robot interface

A walking companion robot is presented for rehabilitation from dyskinesia of lower limbs in this article. A new human–robot interface (HRI) is designed which adopts one-axis force sensor and potentiometer connector to detect the motion of the user. To accompany in displacement and angle between the user and the robot precisely in real time, the common motions are classified into two elemental motion states. With distinction method of motion states, a classification scheme of motion control is adopted. The mathematical model-based control method is first introduced and the corresponding control systems are built. Due to the unavoidable deviation of the mathematical model-based control method, a force control method is proposed and the corresponding control systems are built. The corresponding simulations demonstrate that the efficiency of the two proposed control methods. The experimental data and paths of robot verify the two control methods and indicate that the force control method can better satisfy the user’s requirements.

Robot social-aware navigation framework to accompany people walking side-by-side

We present a novel robot social-aware navigation framework to walk side-by-side with people in crowded urban areas in a safety and natural way. The new system includes the following key issues: to propose a new robot social-aware navigation model to accompany a person; to extend the Social Force Extended Social-Force Model, to consider the person and robot's interactions; to use a human predictor to estimate the destination of the person the robot is walking with; and to interactively learning the parameters of the social-aware navigation model using multimodal human feedback. Finally, a quantitative metric based on people's personal spaces and comfortableness criteria, is introduced in order to evaluate quantitatively the performance of the robot's task. The validation of the model is accomplished throughout an extensive set of simulations and real-life experiments. In addition, a volunteers' survey is used to measure the acceptability of our robot companion's behavior.

Science Class with RoboThespian: Using a Robot Teacher to Make Science Fun and Engage Students

There is growing interest in observing how interaction with a robot can enhance human learning. Recent studies of educational processes mediated by human-like robots (HLRs) focused mainly on individual learning with robot companions. This article presents a study of a science lesson mediated by the life-size humanoid robot, RoboThespian. Participants in the study were school groups from grades 5-7 who participated in a lesson at the science museum MadaTech in two different classroom environments. The study evaluated the effectiveness in learning new science concepts and creating positive perceptions of the robot teacher. The results show that the lesson achieved its educational objectives. The level of learning interaction was influenced by the design of learning activities, robot behaviors, and the students' perceived psychological distance from the robot.

Must smart objects look human? Study of the impact of anthropomorphism on the acceptance of companion robots

Smart products will play a central role in consumer living in years to come. Close to humans because of their “intelligence,” should they also look like humans? The aim of this research is to test the impact of anatomical anthropomorphism on the acceptance of an autonomous product, the companion robot, measured by its perceived usefulness, ease of use, and use intentions. Our results show that, unlike previous marketing research on traditional product anthropomorphism, the human appearance of a companion robot does not increase its acceptance by consumers. However, we show that a partially anthropomorphic appearance improves acceptance by people with practical experience of similar technology (the smartphone), while it reduces acceptance by other people. Our results also emphasize the difficulty of extending research on the impact of anthropomorphism on consumers. They also highlight the need to integrate appearance in acceptance models, and they show how experience with products similar to the new one influences the impact of appearance. Finally, we show the role of the categorization process in the impact of appearance.

Adaptive artificial companions learning from users’ feedback

Until recently, propositions on the subject of intelligent service companions, like robots, were mostly user and environment independent. Our work is part of the FUI-RoboPopuli project, which concentrates on endowing entertainment companion robots with adaptive and social behavior. More precisely, we focus on the capacity of an intelligent system to learn how to personalize and adapt its behavior/actions according to its interaction situation that describes (a) the current user attributes, and (b) the current environment attributes. Our approach is based on models of the type of Markov decision processes (MDPs) that are largely used for adaptive robot applications. In order to have, as quickly as possible, a relevant adaptive behavior whatever the interaction situation, several approaches were proposed to decrease the sample complexity required to learn the MDP model, including its reward function. We argue that systems that are based on detecting important attributes for each decision are more likely to converge faster than others. To this end, we present two algorithms to learn the MDP reward function through analyzing interaction traces (i.e., the interaction history between the robot and its users including their feedback regarding the robot actions). The first algorithm is direct, certain and does not particularly exploit its knowledge to adapt to unknown situations (i.e., unknown users’ and/or environment settings). The second is able to detect the importance of certain situation attributes in the adaptation process. The detection of important attributes is used to speed up the learning process and helps by generalizing the learned reward function to unknown situations. In this paper, we present both learning algorithms, simulated experiments and an experiment with the EMOX (EMOtion eXchange) robot that was upgraded during the FUI-RoboPopuli project. The results of those experiments prove that when dealing with adaptive decision making, the detection of important attributes for each decision speeds up the learning process and help in achieving convergence using fewer samples. We also present a scaling analysis through the simulated experiments.

Embedding Ethical Principles in Collective Decision Support Systems

The future will see autonomous machines acting in the same environment as humans, in areas as diverse as driving, assistive technology, and health care. Think of self-driving cars, companion robots, and medical diagnosis support systems. We also believe that humans and machines will often need to work together and agree on common decisions. Thus hybrid collective decision making systems will be in great need. In this scenario, both machines and collective decision making systems should follow some form of moral values and ethical principles (appropriate to where they will act but always aligned to humans'), as well as safety constraints. In fact, humans would accept and trust more machines that behave as ethically as other humans in the same environment. Also, these principles would make it easier for machines to determine their actions and explain their behavior in terms understandable by humans. Moreover, often machines and humans will need to make decisions together, either through consensus or by reaching a compromise. This would be facilitated by shared moral values and ethical principles.