scholarly journals How Much Information Does a Robot Need? Exploring the Benefits of Increased Sensory Range in a Simulated Crowd Navigation Task

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 112
Author(s):  
Marit Hagens ◽  
Serge Thill
Keyword(s):  
Task Performance ◽  
Human Robot Interaction ◽  
The Other ◽  
Proof Of Concept ◽  
Robot Interaction ◽  
Navigation Task ◽  
Navigation Strategy ◽  
Hardware Designs ◽  
Navigation Strategies ◽  
And Task

Perfect information about an environment allows a robot to plan its actions optimally, but often requires significant investments into sensors and possibly infrastructure. In applications relevant to human–robot interaction, the environment is by definition dynamic and events close to the robot may be more relevant than distal ones. This suggests a non-trivial relationship between sensory sophistication on one hand, and task performance on the other. In this paper, we investigate this relationship in a simulated crowd navigation task. We use three different environments with unique characteristics that a crowd navigating robot might encounter and explore how the robot’s sensor range correlates with performance in the navigation task. We find diminishing returns of increased range in our particular case, suggesting that task performance and sensory sophistication might follow non-trivial relationships and that increased sophistication on the sensor side does not necessarily equal a corresponding increase in performance. Although this result is a simple proof of concept, it illustrates the benefit of exploring the consequences of different hardware designs—rather than merely algorithmic choices—in simulation first. We also find surprisingly good performance in the navigation task, including a low number of collisions with simulated human agents, using a relatively simple A*/NavMesh-based navigation strategy, which suggests that navigation strategies for robots in crowds need not always be sophisticated.

scholarly journals GazeEMD: Detecting Visual Intention in Gaze-Based Human-Robot Interaction

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 68
Author(s):  
Lei Shi ◽  
Cosmin Copot ◽  
Steve Vanlanduit
Keyword(s):  
Cognitive Load ◽  
Real World ◽  
Robotic Manipulator ◽  
Similarity Score ◽  
Human Robot Interaction ◽  
Experimental Results ◽  
The Other ◽  
Robot Interaction ◽  
Run Length ◽  
The Real

In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario.

Individual Differences in Human-Robot Interaction in a Military Multitasking Environment

2011 ◽  
Vol 5 (1) ◽  
pp. 83-105 ◽  
Author(s):  
Jessie Y. C. Chen
Keyword(s):  
Task Performance ◽  
Attentional Control ◽  
Target Recognition ◽  
Human Robot Interaction ◽  
Automated Systems ◽  
Robot Interaction ◽  
Military Vehicle ◽  
And Performance ◽  
Modality Preference ◽  
And Robotics

A military vehicle crew station environment was simulated and a series of three experiments was conducted to examine the workload and performance of the combined position of the gunner and robotics operator in a multitasking environment. The study also evaluated whether aided target recognition (AiTR) capabilities (delivered through tactile and/or visual cuing) for the gunnery task might benefit the concurrent robotics and communication tasks and how the concurrent task performance might be affected when the AiTR was unreliable (i.e., false alarm prone or miss prone). Participants’ spatial ability was consistently found to be a reliable predictor of their targeting task performance as well as their modality preference for the AiTR display. Participants’ attentional control was found to significantly affect the way they interacted with unreliable automated systems.

Interaction of Arousal and Task Difficulty upon Perceptual Restriction

1971 ◽  
Vol 32 (1) ◽  
pp. 259-264
Author(s):  
Gerald M. Long ◽  
Suchoon S. Mo
Keyword(s):  
Task Performance ◽  
Significant Interaction ◽  
Task Difficulty ◽  
Arousal Level ◽  
The Other ◽  
Stress Group ◽  
Cue Utilization ◽  
Central Task ◽  
Ice Water ◽  
And Task

Ss were required to perform both central and peripheral perceptual tasks presented simultaneously by means of 200-msec. tachistoscopic flashes. The central task, requiring the discrimination between 2 lines on the basis of length, was progressively increased in difficulty from Test I to Test III. Peripheral task performance required the estimation of the number of black dots surrounding the central task. This number varied randomly between 1 and 8. Half the Ss in each test performed these tasks under stress (hand in ice-water), the other Ss under no stress. A significant interaction ( p < .01) was found; the stress group was inferior to the no-stress group on the peripheral task when the central task was relatively easy (Test I) but was superior to the no-stress group when the central task was extremely difficult (Test III). The results were interpreted in terms of the dependence of the “range of cue utilization” upon the degree of difficulty of the perceptual material as well as arousal level.

Expression and Identification of Confidence Based on Individual Verbal and Non-Verbal Features in Human-Robot Interaction

2019 ◽  
Vol 23 (6) ◽  
pp. 1089-1097
Author(s):  
Youdi Li ◽  
◽  
Wei Fen Hsieh ◽  
Eri Sato-Shimokawara ◽  
Toru Yamaguchi
Keyword(s):  
Daily Life ◽  
Expression Patterns ◽  
Individual Characteristics ◽  
Human Robot Interaction ◽  
The Other ◽  
Robot Interaction ◽  
Psychological Feature ◽  
Confidence Degree ◽  
Distinct Features ◽  
Proper Response

In our daily life, it is inevitable to confront the condition which we feel confident or unconfident. Under these conditions, we might have different expressions and responses. Not to mention under the situation when a human communicates with a robot. It is necessary for robots to behave in various styles to show adaptive confidence degree, for example, in previous work, when the robot made mistakes during the interaction, different certainty expression styles have shown influence on humans’ truthfulness and acceptance. On the other hand, when human feel uncertain on the robot’s utterance, the approach of how the robot recognizes human’s uncertainty is crucial. However, relative researches are still scarce and ignore individual characteristics. In current study, we designed an experiment to obtain human verbal and non-verbal features under certain and uncertain condition. From the certain/uncertain answer experiment, we extracted the head movement and voice factors as features to investigate if we can classify these features correctly. From the result, we have found that different people had distinct features to show different certainty degree but some participants might have a similar pattern considering their relatively close psychological feature value. We aim to explore different individuals’ certainty expression patterns because it can not only facilitate humans’ confidence status detection but also is expected to be utilized on robot side to give the proper response adaptively and thus spice up the Human-Robot Interaction.

scholarly journals Use and usability of software verification methods to detect behaviour interference when teaching an assistive home companion robot: A proof-of-concept study

2021 ◽  
Vol 12 (1) ◽  
pp. 402-422
Author(s):  
Kheng Lee Koay ◽  
Matt Webster ◽  
Clare Dixon ◽  
Paul Gainer ◽  
Dag Syrdal ◽  
...  
Keyword(s):  
Software Verification ◽  
User Study ◽  
Human Robot Interaction ◽  
Proof Of Concept ◽  
Robot Interaction ◽  
Teaching System ◽  
Assistive Robots ◽  
Verification Methods ◽  
Smart Home Environment ◽  
Formal Behaviour

Abstract When studying the use of assistive robots in home environments, and especially how such robots can be personalised to meet the needs of the resident, key concerns are issues related to behaviour verification, behaviour interference and safety. Here, personalisation refers to the teaching of new robot behaviours by both technical and non-technical end users. In this article, we consider the issue of behaviour interference caused by situations where newly taught robot behaviours may affect or be affected by existing behaviours and thus, those behaviours will not or might not ever be executed. We focus in particular on how such situations can be detected and presented to the user. We describe the human–robot behaviour teaching system that we developed as well as the formal behaviour checking methods used. The online use of behaviour checking is demonstrated, based on static analysis of behaviours during the operation of the robot, and evaluated in a user study. We conducted a proof-of-concept human–robot interaction study with an autonomous, multi-purpose robot operating within a smart home environment. Twenty participants individually taught the robot behaviours according to instructions they were given, some of which caused interference with other behaviours. A mechanism for detecting behaviour interference provided feedback to participants and suggestions on how to resolve those conflicts. We assessed the participants’ views on detected interference as reported by the behaviour teaching system. Results indicate that interference warnings given to participants during teaching provoked an understanding of the issue. We did not find a significant influence of participants’ technical background. These results highlight a promising path towards verification and validation of assistive home companion robots that allow end-user personalisation.

scholarly journals Drawing parallels in human–other interactions: a trans-disciplinary approach to developing human–robot interaction methodologies

2019 ◽  
Vol 374 (1771) ◽  
pp. 20180433 ◽  
Author(s):  
Emily C. Collins
Keyword(s):  
Social Cognition ◽  
Attachment Styles ◽  
Empirical Work ◽  
Human Robot Interaction ◽  
Proof Of Concept ◽  
Robot Interaction ◽  
Methodology Development ◽  
Psychological Measures ◽  
Object Interaction ◽  
Human Object

This opinion paper discusses how human–robot interaction (HRI) methodologies can be robustly developed by drawing on insights from fields outside of HRI that explore human–other interactions. The paper presents a framework that draws parallels between HRIs, and human–human, human–animal and human–object interaction literature, by considering the morphology and use of a robot to aid the development of robust HRI methodologies. The paper then briefly presents some novel empirical work as proof of concept to exemplify how the framework can help researchers define the mechanism of effect taking place within specific HRIs. The empirical work draws on known mechanisms of effect in animal-assisted therapy, and behavioural observations of touch patterns and their relation to individual differences in caring and attachment styles, and details how this trans-disciplinary approach to HRI methodology development was used to explore how an interaction with an animal-like robot was impacting a user. In doing so, this opinion piece outlines how useful objective, psychological measures of social cognition can be for deepening our understanding of HRI, and developing richer HRI methodologies, which take us away from questions that simply ask ‘Is this a good robot?’, and closer towards questions that ask ‘What mechanism of effect is occurring here, through which effective HRI is being performed?’ This paper further proposes that in using trans-disciplinary methodologies, experimental HRI can also be used to study human social cognition in and of itself. This article is part of the theme issue ‘From social brains to social robots: applying neurocognitive insights to human–robot interaction’.

scholarly journals Emotion based human-robot interaction

2018 ◽  
Vol 161 ◽  
pp. 01001 ◽  
Author(s):  
Karsten Berns ◽  
Zuhair Zafar
Keyword(s):  
Humanoid Robot ◽  
Humanoid Robots ◽  
Verbal Communication ◽  
Human Robot Interaction ◽  
The Other ◽  
Robot Interaction ◽  
Human Machine Interaction ◽  
Verbal Cues ◽  
Perception System ◽  
Machine Interaction

Human-machine interaction is a major challenge in the development of complex humanoid robots. In addition to verbal communication the use of non-verbal cues such as hand, arm and body gestures or mimics can improve the understanding of the intention of the robot. On the other hand, by perceiving such mechanisms of a human in a typical interaction scenario the humanoid robot can adapt its interaction skills in a better way. In this work, the perception system of two social robots, ROMAN and ROBIN of the RRLAB of the TU Kaiserslautern, is presented in the range of human-robot interaction.

Framework for Interaction Among Human–Robot-Environment in DigiLog Space

2014 ◽  
Vol 11 (04) ◽  
pp. 1442005 ◽  
Author(s):  
Youngho Lee ◽  
Young Jae Ryoo ◽  
Jongmyung Choi
Keyword(s):  
Real Time ◽  
Robot Control ◽  
Smart Phone ◽  
Human Robot Interaction ◽  
The Other ◽  
Computing Technology ◽  
Robot Interaction ◽  
Direct Communication ◽  
Human Interactions ◽  
Remote Controller

With the development of computing technology, robots are now popular in our daily life. Human–robot interaction is not restricted to a direct communication between them. The communication could include various different human to human interactions. In this paper, we present a framework for enhancing the interaction among human–robot-environments. The proposed framework is composed of a robot part, a user part, and the DigiLog space. To evaluate the proposed framework, we applied the framework into a real-time remote robot-control platform in the smart DigiLog space. We are implementing real time controlling and monitoring of a robot by using one smart phone as the robot brain and the other smart phone as the remote controller.

scholarly journals Coordinating Entrainment Phenomena: Robot Conversation Strategy for Object Recognition

2021 ◽  
Vol 11 (5) ◽  
pp. 2358
Author(s):  
Mitsuhiko Kimoto ◽  
Takamasa Iio ◽  
Masahiro Shiomi ◽  
Katsunori Shimohara
Keyword(s):  
Object Recognition ◽  
Recognition Performance ◽  
Human Robot Interaction ◽  
The Other ◽  
Interactive Approach ◽  
Robot Interaction ◽  
Human Research ◽  
Amount Of Information ◽  
Development Approach ◽  
New Devices

This study proposes a robot conversation strategy involving speech and gestures to improve a robot’s indicated object recognition, i.e., the recognition of an object indicated by a human. Research conducted to improve the performance of indicated object recognition is divided into two main approaches: development and interactive. The development approach addresses the development of new devices or algorithms. Through human–robot interaction, the interactive approach improves the performance by decreasing the variability and the ambiguity of the references. Inspired by the findings of entrainment and entrainment inhibition, this study proposes a robot conversation strategy that utilizes the interactive approach. While entrainment is a phenomenon in which people unconsciously tend to mimic words and/or gestures of their interlocutor, entrainment inhibition is the opposite phenomenon in which people decrease the amount of information contained in their words and gestures when their interlocutor provides excess information. Based on these phenomena, we designed a robot conversation strategy that elicits clear references. We experimentally compared this strategy with the other interactive strategy in which a robot explicitly requests clarifications when a human refers to an object. We obtained the following findings: (1) The proposed strategy clarifies human references and improves indicated object recognition performance, and (2) the proposed strategy forms better impressions than the other interactive strategy that explicitly requests clarifications when people refer to objects.

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