Encoding guidelines for a culturally competent robot for elderly care

The functionalities and behaviours of socially assistive robots for the care of older people are usually defined by the robot’s designers with limited room for runtime adaptation to meet the preferences, expectations and needs of the assisted person. However, adaptation plays a crucial role for the robot’s acceptability and ultimately for its effectiveness. Culture, which deeply influences a person’s preferences and habits, can be viewed as an invaluable “enabling technology” to achieve such level of adaptation. This paper discusses how guidelines describing culturally competent assistive behaviours can be encoded in a robot to effectively tune its actions, gestures and words. The proposed system is implemented on a Pepper robot and tested with an Indian persona, whose habits and preferences the robot discovers and adapts to at runtime

Machiavellian Robots and Their Theory of Mind

The objective of this work is to develop and evaluate computational cognitive models of Theory of Mind (ToM) and Machiavellian behavior embedded in a humanoid robot. Machiavellianism, together with psychopathy and narcissism, is part of the Dark Triad (DT), three constructs that correspond to socially aversive yet not necessarily pathological personalities. The motivations of the present work are both theoretical and application-oriented. In the long term, we aim to: (i) Provide researchers with new insights into the Machiavellian as well as other DT constructs through simulated and robotic setups; (ii) Provide a tool to train psychologists to deal with social and antisocial behavior in a controlled setup; (iii) Help people become aware of the behavioral mechanisms that they may expect from people with DT traits in social and affective relationships; (iv) Assist robotic engineers in developing better robots by identifying behaviors that should be avoided. To this end, we explored a computational model of ToM in the popular Planning Domain Definition Language (PDDL), and defined a domain with the necessary elements to induce Machiavellian behavior during planning and execution. Subsequently, we implemented our computational model in a software architecture controlling the behavior of a humanoid robot and recorded videos of the robot interacting with two actors. Finally, we conducted experiments with 300 participants divided into 6 conditions to verify whether the implemented framework is versatile enough to generate behaviors that participants would rate as either more Machiavellian or less Machiavellian based on their observations of the recorded videos

Culture as a Sensor? A Novel Perspective on Human Activity Recognition

Human Activity Recognition (HAR) systems are devoted to identifying, amidst the sensory stream provided by one or more sensors located so that they can monitor the actions of a person, portions related to the execution of a number of a-priori defined activities of interest. Improving the performance of systems for Human Activity Recognition is a long-standing research goal: solutions include more accurate sensors, more sophisticated algorithms for the extraction and analysis of relevant information from the sensory data, and the enhancement of the sensory analysis with general or person-specific knowledge about the execution of the activities of interest. Following the latter trend, in this article we propose the association and enhancement of the sensory data analysis with cultural information, that can be seen as an estimate of person-specific information, relieved of the burden of a long/complex setup phase. We propose a culture-aware Human Activity Recognition system which associates the recognition response provided by a state-of-the-art, culture-unaware HAR system with culture-specific information about where and when activities are most likely performed in different cultures, encoded in an ontology. The merging of the cultural information with the culture-unaware responses is done by a Bayesian Network, whose probabilistic approach allows for avoiding stereotypical representations. Experiments performed offline and online, using images acquired by a mobile robot in an apartment, show that the culture-aware HAR system consistently outperforms the culture-unaware HAR system

Knowledge representation for culturally competent personal robots: requirements, design principles, implementation, and assessment

Culture, intended as the set of beliefs, values, ideas, language, norms and customs which compose a person’s life, is an essential element to know by any robot for personal assistance. Culture, intended as that person’s background, can be an invaluable source of information to drive and speed up the process of discovering and adapting to the person’s habits, preferences and needs. This article discusses the requirements posed by cultural competence on the knowledge management system of a robot. We propose a framework for cultural knowledge representation that relies on (i) a three layer ontology for storing concepts of relevance, culture specific information and statistics, person-specific information and preferences; (ii) an algorithm for the acquisition of person-specific knowledge, which uses culture specific knowledge to drive the search; (iii) a Bayesian Network for speeding up the adaptation to the person by propagating the effects of acquiring one specific information onto interconnected concepts. We have conducted a preliminary evaluation of the framework involving 159 Italian and German volunteers and considering 122 among habits, attitudes and social norms

Designing an Experimental and a Reference Robot to Test and Evaluate the Impact of Cultural Competence in Socially Assistive Robotics

The article focusses on the work performed in preparation for an experimental trial aimed at evaluating the impact of a culturally competent robot for care home assistance. Indeed, it has been estabilished that the user's cultural identity plays an important role during the interaction with a robotic system and cultural competence may be one of the key elements for increasing capabilities of socially assistive robots. Specifically, the paper describes part of the work carried out for the definition and implementation of two different robotic systems for the care of older adults: a culturally competent robot, that shows its awareness of the user's cultural identity, and a reference robot, non culturally competent, but with the same functionalities of the former. The design of both robots is here described in detail, together with the key elements that make a socially assistive robot culturally competent, which should be absent in the non-culturally competent counterpart. Examples of the experimental phase of the CARESSES project, with a fictional user are reported, giving a hint of the validness of the proposed approach

Development of a fully autonomous culturally competent robot companion

This chapter addresses the problem of producing fully autonomous, intelligent robots: that is, robots equipped with sensors, actuators, and Artificial Intelligence programs allowing them to perceive the environment, make decisions, and act without being teleoperated or following a script. First, we provide a brief review of today's robotic technology by discussing why most robots shown in the media should be viewed as outstanding examples of mechanics and control that, however, are neither intelligent nor autonomous. Then, we follow the steps from creating a prototype in a research lab to deploying a robot operating 24/7 in the real world: we discuss the technological challenges and propose smart solutions to achieve the biggest impact with the slightest effort. Finally, we illustrate some ideas for the future

Usability evaluation with different viewpoints of a Human-Swarm interface for UAVs control in formation

A common way to organize a high number of robots, both when moving autonomously and when controlled by a human operator, is to let them move in formation. This is a principle that takes inspiration from the nature, that maximizes the possibility of monitoring the environment and therefore of anticipating risks and finding targets. In robotics, alongside these reasons, the organization of a robot team in a formation allows a human operator to deal with a high number of agents in a simpler way, moving the swarm as a single entity. In this context, the typology of visual feedback is fundamental for a correct situational awareness, but in common practice having an optimal camera configuration is not always possible. Usually human operators use cameras on board the multirotors, with an egocentric point of view, while it is known that in mobile robotics overall awareness and pattern recognition are optimized by exocentric views. In this article we present an analysis of the performance achieved by human operators controlling a swarm of UAVs in formation, accomplishing different tasks and using different point of views. The control architecture is implemented in a ROS framework and interfaced with a 3D simulation environment. Experimental tests show a degradation of performance while using egocentric cameras with respect of an exocentric point of view, although cameras on board the robots allow to satisfactorily accomplish simple tasks

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