Sensemaking Through Motion in Human-Robot Interaction (original) (raw)
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Springer eBooks, 2015
The evaluation of subjective feelings of discomfort is a serious problem in the field of ergonomics, environment engineering and human-robot interactions. It is difficult to measure them by using objective quantifications in a traditional sense. We have hypothesized that the unpleasant feeling emerges caused by deviating from the autonomy, which exists in the perception-action cycle, the convective flow in two layers of intuition and perception and continuity in the dialogue. The present paper discusses the issue with respect to two examples; an unexpected breaking of balance in the trained motion and irritation at the lack of progress by discontinuity in the dialogue, and attempts to build a theoretical framework to detect a deviation from ongoing autonomy. This paper discusses a way of the theoretical modeling and explores possible implementations into the robotic experiment that reproduces human behaviors in need of supervision by the central nervous system. This preliminary report can contribute to an extension of the coupling between the central and peripheral nervous systems to social communications which is supported by assistive technologies and accessibility improvements. Keywords: Assistive robots Á Principle component analysis Á Dynamical systems approach Á Emotion Á Levels of cognitive involvement
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… of the 2005 joint conference on …, 2005
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This paper describes a nonverbal approach for a robot to inform a human of its internal state. It is implemented by executing particular motion. In prac- tical cooperation of a human and a robot, the robot often requires human's help to achieve a task. In such a situation, a robot needs to send useful information on its internal state like intention, to guide a human to understand it and to make him/her to execute actions for help. A simple solution for how to inform a hu- man of the robot's internal state is an explicit way in which a robot sends verbal information like speech syn- thesis. However, in terms of human-robot interaction, implicit and eective approach is preferable because it must be more natural and inexpensive like the interac- tion between humans. Thus we propose a motion-based approach for a robot to inform a human of its internal state. By such an approach, a robot is able to tell the internal state to a human naturally, and he/she can understand the rob...
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The topic of Human-Robot Interaction is currently widely researched. This is the field in technology that aims to improve interaction between humans and robots in such a way that it is beneficial for the society. It combines research from various different disciplines such as mechanical engineering, computer science, electrical engineering, psychology, biology, etcetera (Sheridan, 2016). Robotics and human-robot interaction are finding great use for the AI subfield of machine learning. Robot perception, behaviour, and interactivity between several robots can all be developed using machine learning.
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In recent years, an increased effort has been invested to improve the capabilities of robots. Nevertheless, human-robot interaction remains a complex field of application where errors occur frequently. The reasons for these errors can primarily be divided into two classes. Foremost, the recent increase in capabilities also widened possible sources of errors on the robot's side. This entails problems in the perception of the world, but also faulty behavior, based on errors in the system. Apart from that, non-expert users frequently have incorrect assumptions about the functionality and limitations of a robotic system. This leads to incompatibilities between the user's behavior and the functioning of the robot's system, causing problems on the robot's side and in the human-robot interaction. While engineers constantly improve the reliability of robots, the user's understanding about robots and their limitations have to be addressed as well. In this work, we investigate ways to improve the understanding about robots. For this, we employ FAMILIAR-FunctionAl user Mental model by Increased LegIbility ARchitecture, a transparent robot architecture with regard to the robot behavior and decision-making process. We conducted an online simulation user study to evaluate two complementary approaches to convey and increase the knowledge about this architecture to non-expert users: a dynamic visualization of the system's processes as well as a visual programming interface. The results of this study reveal that visual programming improves knowledge about the architecture. Furthermore, we show that with increased knowledge about the control architecture of the robot, users were significantly better in reaching the interaction goal. Furthermore, we showed that anthropomorphism may reduce interaction success.
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An approach is presented for the expression of basic emotions through only the agent body pose and velocity. The approach is applied in human-robot interaction scenarios, where both humans and robots communicate only through their relative position and velocities. As a result, an interface for human-robot interaction is obtained, which does not require the use of haptic devices or explicit communication with humans, verbal for instance. The small set of emotions that can be conveyed enable humans and robots to anticipate the intentions of the opponent and adapt their behavior accordingly. The aproach is implemented using a webcam, simple vision processing algorithms and Hidden Markov models. The results of preliminary experiments are presented.
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The experience of interacting with a robot has been shown to be very different in comparison to people's interaction experience with other technologies and artifacts, and often has a strong social or emotional componenta difference that poses potential challenges related to the design and evaluation of HRI. In this paper we explore this difference, and its implications on evaluating HRI. We outline how this difference is due in part to the general complexity of robots' overall context of interaction, related to their dynamic presence in the real world and their tendency to invoke a sense of agency.
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Current research in human–robot interaction often focuses on rendering communication between humans and robots more ‘natural’ by designing machines that appear and behave humanlike. Communication, in this human-centric approach, is often understood as a process of successfully transmitting information in the form of predefined messages and gestures. This article introduces an alternative arts-led, movement-centric approach, which embraces the differences of machinelike robotic artefacts and, instead, investigates how meaning is dynamically enacted in the encounter of humans and machines. Our design approach revolves around a novel embodied mapping methodology, which serves to bridge between human–machine asymmetries and socioculturally situate abstract robotic artefacts. Building on concepts from performativity, material agency, enactive sense-making and kinaesthetic empathy, our Machine Movement Lab project opens up a performative-relational model of human–machine communication, wh...
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The experience of interacting with a robot has been shown to be very different in comparison to people's interaction experience with other technologies and artifacts, and often has a strong social or emotional component-a fact that raises concerns related to evaluation. In this paper we outline how this difference is due in part to the general complexity of robots' overall context of interaction, related to their dynamic presence in the real world and their tendency to invoke a sense of agency. A growing body of work in Human-Robot Interaction (HRI) focuses on exploring this overall context and tries to unpack what exactly is unique about interaction with robots, often through leveraging evaluation methods and frameworks designed for more-traditional HCI. We raise the concern that, due to these differences, HCI evaluation methods should be applied to HRI with care, and we present a survey of HCI evaluation tech