Implications of the Google’s US 8,996,429 B1 Patent in Cloud Robotics-Based Therapeutic Researches

Intended for being informative to both legal and engineer communities, this chapter raises awareness on the implications of recent patents in the field of human-robot interaction (HRI) studies. Google patented the use of cloud robotics to create robot personality(-ies). The broad claims of the patent could hamper many HRI research projects in the field. One of the possible frustrated research lines is related to robotic therapies because the personalization of the robot accelerates the process of engagement, which is extremely beneficial for robotic cognitive therapies. This chapter presents, therefore, the scientific examination, description, and comparison of the Tufts University CEEO project “Data Analysis and Collection through Robotic Companions and LEGO® Engineering with Children on the Autism Spectrum project” and the US 8,996,429 B1 Patent on the Methods and Systems for Robot Personality Development of Google. Some remarks on ethical implications of the patent will close the chapter and open the discussion to both communities

Externalising moods and psychological states in a cloud based system to enhance a pet-robot and child’s interaction

Background:This PATRICIA research project is about using pet robots to reduce pain and anxiety in hospitalized children. The study began 2 years ago and it is believed that the advances made in this project are significant. Patients, parents, nurses, psycholo- gists, and engineers have adopted the Pleo robot, a baby dinosaur robotic pet, which works in different ways to assist children during hospitalization. Methods: Focus is spent on creating a wireless communication system with the Pleo in order to help the coordinator, who conducts therapy with the child, monitor, under- stand, and control Pleo’s behavior at any moment. This article reports how this techno- logical function is being developed and tested. Results: Wireless communication between the Pleo and an Android device is achieved. The developed Android app allows the user to obtain any state of the robot without stopping its interaction with the patient. Moreover, information is sent to a cloud, so that robot moods, states and interactions can be shared among different robots. Conclusions: Pleo attachment was successful for more than 1 month, working with children in therapy, which makes the investment capable of positive therapeutic possibilities. This technical improvement in the Pleo addresses two key issues in social robotics: needing an enhanced response to maintain the attention and engagement of the child, and using the system as a platform to collect the states of the child’s progress for clinical purposes.Peer ReviewedPostprint (published version

How Humans Judge Machines

How people judge humans and machines differently, in scenarios involving natural disasters, labor displacement, policing, privacy, algorithmic bias, and more. How would you feel about losing your job to a machine? How about a tsunami alert system that fails? Would you react differently to acts of discrimination depending on whether they were carried out by a machine or by a human? What about public surveillance? How Humans Judge Machines compares people's reactions to actions performed by humans and machines. Using data collected in dozens of experiments, this book reveals the biases that permeate human-machine interactions. Are there conditions in which we judge machines unfairly? Is our judgment of machines affected by the moral dimensions of a scenario? Is our judgment of machine correlated with demographic factors such as education or gender? César Hidalgo and colleagues use hard science to take on these pressing technological questions. Using randomized experiments, they create revealing counterfactuals and build statistical models to explain how people judge artificial intelligence and whether they do it fairly. Through original research, How Humans Judge Machines bring us one step closer to understanding the ethical consequences of AI. Written by César A. Hidalgo, the author of Why Information Grows and coauthor of The Atlas of Economic Complexity (MIT Press), together with a team of social psychologists (Diana Orghian and Filipa de Almeida) and roboticists (Jordi Albo-Canals), How Humans Judge Machines presents a unique perspective on the nexus between artificial intelligence and society. Anyone interested in the future of AI ethics should explore the experiments and theories in How Humans Judge Machines

Pain and anxiety treatment based on social robot interaction with children to improve patient experience. Ongoing research

A major focus for children’s quality of life programs in hospitals is improving their experiences during procedures. In anticipation of treatment, children may become anxious and during procedures pain appears. The aim of this article is to introduce a proposal to design pioneering techniques based on the use of social robots to improve the patient experience by eliminating or minimizing pain and anxiety. According to this proposed challenge, this research aims to design and develop specific human-social robot interaction with pet robots. Robot interactive behavior will be designed based on modular skills using soft-computing paradigms.Postprint (published version

Modelling social skills and problem solving strategies used by children with ASD through cloud connected social robots as data loggers: first modelling approach

In this paper, we present a set up of cloudconnected social robots to measure and model the effect of LEGO Engineering and its collaborative nature on the development of social skills in children with Autism Spectrum Disorder (ASD). Here we introduce the first approach to the modelling process designed.Postprint (published version

Evaluating Group-Robot Interaction in Crowded Public Spaces: A Week-Long Exploratory Study in the Wild with a Humanoid Robot Guiding Visitors Through a Science Museum

Postprint (published version

“I’ll take care of you,” said the robot

The insertion of robotic and artificial intelligent (AI) systems in therapeutic settings is accelerating. In this paper, we investigate the legal and ethical challenges of the growing inclusion of social robots in therapy. Typical examples of such systems are Kaspar, Hookie, Pleo, Tito, Robota,Nao, Leka or Keepon. Although recent studies support the adoption of robotic technologies for therapy and education, these technological developments interact socially with children, elderly or disabled, and may raise concerns that range from physical to cognitive safety, including data protection. Research in other fields also suggests that technology has a profound and alerting impact on us and our human nature. This article brings all these findings into the debate on whether the adoption of therapeutic AI and robot technologies are adequate, not only to raise awareness of the possible impacts of this technology but also to help steer the development and use of AI and robot technologies in therapeutic settings in the appropriate direction. Our contribution seeks to provide a thoughtful analysis of some issues concerning the use and development of social robots in therapy, in the hope that this can inform the policy debate and set the scene for further research

Modelling social skills and problem solving strategies used by children with ASD through cloud connected social robots as data loggers: first modelling approach

In this paper, we present a set up of cloudconnected social robots to measure and model the effect of LEGO Engineering and its collaborative nature on the development of social skills in children with Autism Spectrum Disorder (ASD). Here we introduce the first approach to the modelling process designed

qRobot: A Quantum Computing Approach in Mobile Robot Order Picking and Batching Problem Solver Optimization

This article aims to bring quantum computing to robotics. A quantum algorithm is developed to minimize the distance traveled in warehouses and distribution centers where order picking is applied. For this, a proof of concept is proposed through a Raspberry Pi 4, generating a quantum combinatorial optimization algorithm that saves the distance travelled and the batch of orders to be made. In case of computational need, the robot will be able to parallelize part of the operations in hybrid computing (quantum + classical), accessing CPUs and QPUs distributed in a public or private cloud. We developed a stable environment (ARM64) inside the robot (Raspberry) to run gradient operations and other quantum algorithms on IBMQ, Amazon Braket (D-Wave), and Pennylane locally or remotely. The proof of concept, when run in the above stated quantum environments, showed the execution time of our algorithm with different public access simulators on the market, computational results of our picking and batching algorithm, and analyze the quantum real-time execution. Our findings are that the behavior of the Amazon Braket D-Wave is better than Gate-based Quantum Computing over 20 qubits, and that AWS-Braket has better time performance than Qiskit or Pennylane