Assistive technology design and development for acceptable robotics companions for ageing years

© 2013 Farshid Amirabdollahian et al., licensee Versita Sp. z o. o. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivs license, which means that the text may be used for non-commercial purposes, provided credit is given to the author.A new stream of research and development responds to changes in life expectancy across the world. It includes technologies which enhance well-being of individuals, specifically for older people. The ACCOMPANY project focuses on home companion technologies and issues surrounding technology development for assistive purposes. The project responds to some overlooked aspects of technology design, divided into multiple areas such as empathic and social human-robot interaction, robot learning and memory visualisation, and monitoring persons’ activities at home. To bring these aspects together, a dedicated task is identified to ensure technological integration of these multiple approaches on an existing robotic platform, Care-O-Bot®3 in the context of a smart-home environment utilising a multitude of sensor arrays. Formative and summative evaluation cycles are then used to assess the emerging prototype towards identifying acceptable behaviours and roles for the robot, for example role as a butler or a trainer, while also comparing user requirements to achieved progress. In a novel approach, the project considers ethical concerns and by highlighting principles such as autonomy, independence, enablement, safety and privacy, it embarks on providing a discussion medium where user views on these principles and the existing tension between some of these principles, for example tension between privacy and autonomy over safety, can be captured and considered in design cycles and throughout project developmentsPeer reviewe

Monitoring and Managing Interaction Patterns in Human-Robot Interaction

Nowadays, one of the most challenging problems in Human-Robot Interaction (HRI) is to make robots able to understand humans to successfully accomplish tasks in human environments. HRI has a very different role in all the robotics fields. While autonomous robots do not require a complex HRI system, it is of vital importance for service robots. The goal of this thesis is to study if behavioural patterns that users unconsciously apply when interacting with a robot can be useful to recognise the users' intentions in a particular situation. To carry out this study a prototype has been developed to test in an automatic and objective way, if those interaction patterns performed by several users in the area of service robots are useful to recognise their intentions and disambiguate unclear situations.By using verbal and non-verbal communication that the user unconsciously applies when interacting with a robot, we want to determine automatically what the user is trying to present

A Robotic Writing Framework-Learning Human Aesthetic Preferences via Human-Machine Interactions

Intelligent robots are required to fully understand human intentions and operations in order to support or collaborate with humans to complete complicated tasks, which is typically implemented by employing human-machine interaction techniques. This paper proposes a new robotic learning framework to perform numeral writing tasks by investigating human-machine interactions with human preferences. In particular, the framework implements a trajectory generative module using a generative adversarial network (GAN)-based method and develops a human preference feedback system to enable the robot to learn human preferences. In addition, a convolutional neural network, acting as a discriminative network, classifies numeral images to support the development of the basic numeral writing ability, and another convolutional neural network, acting as a human preference network, learns a human user’s aesthetic preference by taking the feedback on two written numerical images during the training process. The experimental results show that the written numerals based on the preferences of ten users were different from those of the training data set and that the writing models with the preferences from different users generate numerals in different styles, as evidenced by the Fréchet inception distance (FID) scores. The FID scores of the proposed framework with a preference network were noticeably greater than those of the framework without a preference network. This phenomenon indicates that the human-machine interactions effectively guided the robotic system to learn different writing styles. These results prove that the proposed approach is able to enable the calligraphy robot to successfully write numerals in accordance with the preferences of a human user

Monitoring and managing interaction patterns in Human-Robot interaction

Nowadays, one of the most challenging problems in Human-Robot Interaction (HRI) is to make robots able to understand humans to successfully accomplish tasks in human environments. HRI has a very different role in all the robotics fields. While autonomous robots do not require a complex HRI system, it is of vital importance for service robots. The goal of this thesis is to study if behavioural patterns that users unconsciously apply when interacting with a robot can be useful to recognise the users’ intentions in a particular situation. To carry out this study a prototype has been developed to test in an automatic and objective way, if those interaction patterns performed by several users in the area of service robots are useful to recognise their intentions and disambiguate unclear situations