scholarly journals The ANEMONE: Theoretical Foundations for UX Evaluation of Action and Intention Recognition in Human-Robot Interaction

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4284 ◽  
Author(s):  
Jessica Lindblom ◽  
Beatrice Alenljung
Keyword(s):  
User Experience ◽  
Mutual Recognition ◽  
Methodological Approach ◽  
Human Robot Interaction ◽  
Evaluation Framework ◽  
Evaluation Methodology ◽  
Robot Interaction ◽  
Intention Recognition ◽  
Action Model ◽  
Cultural Historical Activity

The coexistence of robots and humans in shared physical and social spaces is expected to increase. A key enabler of high-quality interaction is a mutual understanding of each other’s actions and intentions. In this paper, we motivate and present a systematic user experience (UX) evaluation framework of action and intention recognition between humans and robots from a UX perspective, because there is an identified lack of this kind of evaluation methodology. The evaluation framework is packaged into a methodological approach called ANEMONE (action and intention recognition in human robot interaction). ANEMONE has its foundation in cultural-historical activity theory (AT) as the theoretical lens, the seven stages of action model, and user experience (UX) evaluation methodology, which together are useful in motivating and framing the work presented in this paper. The proposed methodological approach of ANEMONE provides guidance on how to measure, assess, and evaluate the mutual recognition of actions and intentions between humans and robots for investigators of UX evaluation. The paper ends with a discussion, addresses future work, and some concluding remarks.

scholarly journals Should my robot know what's best for me? Human–robot interaction between user experience and ethical design

AI & Society ◽  
2021 ◽  
Author(s):  
Nora Fronemann ◽  
Kathrin Pollmann ◽  
Wulf Loh
Keyword(s):  
User Experience ◽  
Interaction Design ◽  
Real Life ◽  
Well Being ◽  
Design Principles ◽  
Human Robot Interaction ◽  
Ethical Challenges ◽  
Original Design ◽  
Robot Interaction ◽  
Ethical Design

AbstractTo integrate social robots in real-life contexts, it is crucial that they are accepted by the users. Acceptance is not only related to the functionality of the robot but also strongly depends on how the user experiences the interaction. Established design principles from usability and user experience research can be applied to the realm of human–robot interaction, to design robot behavior for the comfort and well-being of the user. Focusing the design on these aspects alone, however, comes with certain ethical challenges, especially regarding the user’s privacy and autonomy. Based on an example scenario of human–robot interaction in elder care, this paper discusses how established design principles can be used in social robotic design. It then juxtaposes these with ethical considerations such as privacy and user autonomy. Combining user experience and ethical perspectives, we propose adjustments to the original design principles and canvass our own design recommendations for a positive and ethically acceptable social human–robot interaction design. In doing so, we show that positive user experience and ethical design may be sometimes at odds, but can be reconciled in many cases, if designers are willing to adjust and amend time-tested design principles.

Socially Embodied Human-Robot Interaction

2015 ◽  
pp. 169-190 ◽  
Author(s):  
J. Lindblom ◽  
B. Alenljung
Keyword(s):  
User Experience ◽  
Human Robot Interaction ◽  
The Body ◽  
Human Interaction ◽  
Future Research ◽  
Crucial Importance ◽  
Robot Interaction ◽  
Use Of Technology ◽  
Fundamental Challenge ◽  
Socially Interactive Robots

A fundamental challenge of human interaction with socially interactive robots, compared to other interactive products, comes from them being embodied. The embodied nature of social robots questions to what degree humans can interact ‘naturally' with robots, and what impact the interaction quality has on the user experience (UX). UX is fundamentally about emotions that arise and form in humans through the use of technology in a particular situation. This chapter aims to contribute to the field of human-robot interaction (HRI) by addressing, in further detail, the role and relevance of embodied cognition for human social interaction, and consequently what role embodiment can play in HRI, especially for socially interactive robots. Furthermore, some challenges for socially embodied interaction between humans and socially interactive robots are outlined and possible directions for future research are presented. It is concluded that the body is of crucial importance in understanding emotion and cognition in general, and, in particular, for a positive user experience to emerge when interacting with socially interactive robots.

How many words can my robot learn?

2007 ◽  
Vol 8 (1) ◽  
pp. 53-81 ◽  
Author(s):  
Luís Seabra Lopes ◽  
Aneesh Chauhan
Keyword(s):  
Word Learning ◽  
Real World ◽  
Scale Up ◽  
Human Robot Interaction ◽  
Learning System ◽  
Evaluation Methodology ◽  
Robot Interaction ◽  
Instructor Feedback ◽  
Discriminatory Capacity ◽  
Robotic Agent

This paper addresses word learning for human–robot interaction. The focus is on making a robotic agent aware of its surroundings, by having it learn the names of the objects it can find. The human user, acting as instructor, can help the robotic agent ground the words used to refer to those objects. A lifelong learning system, based on one-class learning, was developed (OCLL). This system is incremental and evolves with the presentation of any new word, which acts as a class to the robot, relying on instructor feedback. A novel experimental evaluation methodology, that takes into account the open-ended nature of word learning, is proposed and applied. This methodology is based on the realization that a robot’s vocabulary will be limited by its discriminatory capacity which, in turn, depends on its sensors and perceptual capabilities. The results indicate that the robot’s representations are capable of incrementally evolving by correcting class descriptions, based on instructor feedback to classification results. In successive experiments, it was possible for the robot to learn between 6 and 12 names of real-world office objects. Although these results are comparable to those obtained by other authors, there is a need to scale-up. The limitations of the method are discussed and potential directions for improvement are pointed out.

Imitative Neural Mechanism-Based Behavior Intention Recognition System in Human–Robot Interaction

2014 ◽  
Vol 11 (04) ◽  
pp. 1442008 ◽  
Author(s):  
Kwang-Eun Ko ◽  
Kwee-Bo Sim
Keyword(s):  
Mirror Neurons ◽  
Reference Model ◽  
Neural Mechanism ◽  
Recognition System ◽  
Macaque Monkey ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Behavior Intention ◽  
Interaction System ◽  
Intention Recognition

This paper is concerned with an imitative neural mechanism for recognizing behavior intention in human–robot interaction system. The intention recognition process is inspired by the neural mechanism of the mirror neurons in macaque monkey brain. We try to renovate a standard neural network with parametric biases as a reference model to imitate between sensory-motor data pair. The imitation process is primarily directed toward reproducing the goals of observed actions rather than the exact action trajectories. Several experiments and their results show that the proposed model allows to develop useful robotic application for human–robot interaction system application.

Human-Robot Interaction using Intention Recognition

2015 ◽  
Author(s):  
Sangwook Kim ◽  
Zhibin Yu ◽  
Jonghong Kim ◽  
Amitash Ojha ◽  
Minho Lee
Keyword(s):  
Human Robot Interaction ◽  
Robot Interaction ◽  
Intention Recognition
Sign in / Sign up

Export Citation Format

Share Document