Improving HRI through robot architecture transparency (original) (raw)
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Why robots should be technical: Correcting mental models through technical architecture concepts
arXiv (Cornell University), 2020
Research in social robotics is commonly focused on designing robots that imitate human behavior. While this might increase a user's satisfaction and acceptance of robots at first glance, it does not automatically aid a non-expert user in naturally interacting with robots, and might actually hurt their ability to correctly anticipate a robot's capabilities. We argue that a faulty mental model, that the user has of the robot, is one of the main sources of confusion. In this work we investigate how communicating technical concepts of robotic systems to users affects their mental models, and how this can increase the quality of human-robot interaction. We conducted an online study and investigated possible ways of improving users' mental models. Our results underline that communicating technical concepts can form an improved mental model. Consequently, we show the importance of consciously designing robots that express their capabilities and limitations.
Robot Transparency: Improving Understanding of Intelligent Behaviour for Designers and Users
Autonomous robots can be difficult to design and understand. Designers have difficulty decoding the behaviour of their own robots simply by observing them. Naive users of robots similarly have difficulty deciphering robot behaviour simply through observation. In this paper we review relevant robot systems architecture, design, and transparency literature, and report on a programme of research to investigate practical approaches to improve robot transparency. We report on the investigation of real-time graphical and vocalised outputs as a means for both designers and end users to gain a better mental model of the internal state and decision making processes taking place within a robot. This approach, combined with a graphical approach to behaviour design, offers improved transparency for robot designers. We also report on studies of users' understanding, where significant improvement has been achieved using both graphical and vo-calisation transparency approaches.
2006
Models of users are a way to understand and improve the usability of computer interfaces. We present here a model in ACT-R cognitive-modeling language that interacts with a publicly available driving simulation as a simple analog for robot interfaces. The model interacts with the unmodified Java interface by incorporating a novel use of bitmap parsing. The model's structure starts to describe the knowledge a human operator of a robot must have. The model also indicates some of the aspects of the task will be difficult for the operator. For example, the model's performance makes quantitative predictions about how robot speed will influence navigation quality, correlating well to human performance. While the model does not cover all aspects of human-robot interaction, it illustrates how providing user models access to an interface through its bitmap can lead to more accurate and more widely applicable model users.
How to Teach Your Robot in 5 Minutes: Applying UX Paradigms to Human-Robot-Interaction
Proceedings of the IEEE International Symposium on Robot and Human Interactive Communication, 2017
When creating modern and visually appealing user experiences for the interaction with industrial robots, previously known and universally applicable paradigms in app and web design can be utilized to increase accessibility and usability of the to be created service. This is especially the case when the expected user group consists of untrained and inexperienced users and therefore system interaction focus is laid more on build progress overview, safety for human and robot, as well as overall simplification of complicated features. In this paper, we present four of the most important paradigms of modern graphical user experiences in web and app design that can be used to forward the concept of interacting with an industrial robot without any experience-related thresholds. By redesigning an existing interaction concept of a working robot cell system for assembly tasks in a small and medium-sized enterprise environment the presented paradigms are being utilized. The achieved improvements are then examined in a before-after user study to analyze the paradigm's success in suiting the user's expectation and anticipation using the redesigned service.
IEEE RO-MAN 2017, 2017
— Deciphering the behaviour of intelligent others is a fundamental characteristic of our own intelligence. As we interact with complex intelligent artefacts, humans inevitably construct mental models to understand and predict their behaviour. If these models are incorrect or inadequate, we run the risk of self deception or even harm. Here we demonstrate that providing even a simple, abstracted real-time visualisation of a robot's AI can radically improve the transparency of machine cognition. Findings from both an online experiment using a video recording of a robot, and from direct observation of a robot show substantial improvements in observers' understanding of the robot's behaviour. Unexpectedly, this improved understanding was correlated in one condition with an increased perception that the robot was 'thinking', but in no conditions was the robot's assessed intelligence impacted. In addition to our results, we describe our approach, tools used, implications, and potential future research directions.
Shaping Naive Users' Models of Robots' Situation Awareness
Ro Man 2007 the 16th Ieee International Symposium on Robot and Human Interactive Communication, 2007
This paper addresses a so far neglected area of human-robot interaction by approaching situation awareness from the point of view of naïve users. In particular, we present an investigation into which naïve models of robots' capabilities users carry into the interaction, how these models influence the interaction, and which means can be taken to guide users into more realistic models and behaviours if necessary. Quantitative and qualitative investigations reveal not only considerable uncertainty about robots' situation awareness, but also significant differences in dealing with this uncertainty. Three different types of users can be distinguished on the basis of the strategies they take. Finally, we describe experiments with two means of shaping the users' models into more realistic accounts of robots' capabilities. The results suggest that verbal robot output is a powerful means for guiding users subtly, unobtrusively and online into an understanding of robots' capabilities that is more realistic and adequate than users' naïve models of robots' situation awareness.
Intuitive Interfaces in Human-Robot Interaction
2018
This study explores the intuitiveness of four user interfaces (UI) for controlling a mobile robot (BOE Bot): Electromyography, Oculus Rift, joystick, and speech recognition. Intuitiveness was assessed through two means: participants success in navigating the robot through a maze after self-directed training, and scores on usability questionnaires.
Using a simulated user to explore human robot interfaces
2002
Human-robot interfaces (HRI) can be difficult to use. We examine urban search rescue robots (USR) as an example. We present here a theory of their use based on a simulated user written in the ACT-R cognitive modeling language. The model, using a simulated eye and hand, interacts directly with an unmodified and simple tele-operating task of maneuvering in an environment to avoid other moving objects. The model user also performs a secondary task. In addition to describing the knowledge the human operator must have, as well as what aspects of the task will be difficult for the operator, the model makes quantitative predictions about how the speed of the robot influences the quality of the navigation and performance on the secondary task. These results are examples of the types of outputs available from a model user. As the model now interacts with the USR simulator using only the bitmap, the model should be widely applicable to testing other simulators and to actual robots. The model ...
Exploring human mental models of robots through explicitation interviews
19th International Symposium in Robot and Human Interactive Communication, 2010
This paper presents the findings of a qualitative study exploring how mental models of a mechanoid robot using dog-inspired affective cues behaviour emerges and impacts the evaluation of the robot after the viewing of a video of an assistive robotics scenario interaction with the robot. It discusses this using contrasting case studies based on the analysis of explicitation interviews with three participants. The analysis suggests that while for some users zoomorphic cues may aid in initial interactions, they need to be framed in an authentic interaction, highlighting the actual capabilities of the robot as a technological artifact, and how these impact the everyday life and interests of the potential user. 19th IEEE International Symposium on Robot and Human Interactive Communication Principe di Piemonte -Viareggio, Italy, Sept. 12-15, 2010 978-1-4244-7989-4/10/$26.00 ©2010 IEEE 638 978-1-4244-7990-0/10/$26.00 ©2010 IEEE