Using Gesture-Based Interfaces to Control Robots

Bandeira, Gabriel M.; Carmo, Michaela; Ximenes, Bianca Helena; Kelner, Judith

doi:10.1007/978-3-319-20916-6_1

Cited by 11 publications

(7 citation statements)

References 5 publications

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“…To achieve this, NUIs allow users to directly manipulate and interact with robots rather than instruct them to do so by typing commands. Thus, they represent an evolutionary paradigm that overcomes the access bottleneck of classical interaction devices such as keyboards, mice and joysticks, by resorting to voice, gestures, touch and motion tracking [94]. The term TUI encompasses a great variety of interaction systems relying on a coupling between physical objects and digital information, which is physically embodied in concrete form in the environment [95].…”

Section: Interfaces For Robot Programmingmentioning

confidence: 99%

Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications

et al. 2018

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Section: Interfaces For Robot Programmingmentioning

confidence: 99%

Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications

et al. 2018

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“…These interfaces, however: 1) are expensive to waterproof and deploy in underwater environments, 2) require the diver to be close to the AUV, and 3) are cumbersome and unwieldy to operate. Therefore, due to lack of effective technologies of underwater radio and wireless communication, one way for the diver to send commands to an AUV is by means of hand gestures [19]. In response, the AUV can utilize AI to recognize the diver's hand gesture and interpret the corresponding command, as seen in Fig.…”

Section: A Motivationmentioning

confidence: 99%

“…In underwater environments, radio and wireless communications between the diver and AUVs [22] are not efficient. Traditionally, divers used tactile devices to send commands using waterproof tablets, keyboards, mice and joysticks; however, these interfaces are expensive and inconvenient [19]. Therefore, the underwater diver action recognition problem has become an increasingly important area of research to enable efficient underwater diver-AUV interaction.…”

Section: Related Workmentioning

confidence: 99%

DARE: AI-based Diver Action Recognition System using Multi-Channel CNNs for AUV Supervision

Yang¹,

Wilson²,

Gupta³

2020

Preprint

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With the growth of sensing, control and robotic technologies, autonomous underwater vehicles (AUVs) have become useful assistants to human divers for performing various underwater operations. In the current practice, the divers are required to carry expensive, bulky, and waterproof keyboards or joystick-based controllers for supervision and control of AUVs. Therefore, diver action-based supervision is becoming increasingly popular because it is convenient, easier to use, faster, and cost effective. However, the various environmental, diver and sensing uncertainties present underwater makes it challenging to train a robust and reliable diver action recognition system. In this regard, this paper presents DARE, a diver action recognition system, that is trained based on Cognitive Autonomous Driving Buddy (CADDY) dataset, which is a rich set of data containing images of different diver gestures and poses in several different and realistic underwater environments. DARE is based on fusion of stereo-pairs of camera images using a multi-channel convolutional neural network supported with a systematically trained tree-topological deep neural network classifier to enhance the classification performance. DARE is fast and requires only a few milliseconds to classify one stereo-pair, thus making it suitable for real-time underwater implementation. DARE is comparatively evaluated against several existing classifier architectures and the results show that DARE supersedes the performance of all classifiers for diver action recognition in terms of overall as well as individual class accuracies and F1-scores.

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“…The use of social robots has been explored within a museum setting mainly for guiding tours (Yousuf et al , 2013; Pang et al , 2018). According to Levere (2018), robots can answer visitors' questions and tell stories, using voice, gestures (Bandeira et al , 2015), and an interactive touch screen. They also dance, play, and pose for selfies.…”

Section: Introductionmentioning

confidence: 99%

Willingness to accept social robots in museums: an exploratory factor analysis according to visitor profile

Moraleda

Lafuente-Ibáñez

Alvarez

et al. 2021

LHT

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PurposeThe aim of this exploratory study is to identify the factors that influence the acceptance of social robots in museum environments and determine if this influence depends on the visitor's profile (age, gender, education and occupation).Design/methodology/approachData collected from an electronic questionnaire include 433 responses from Spanish visitors. The authors subjected the variables proposed by De Kervenoael et al. (2020) adapted to museums. The initial descriptive analysis only showed significant differences by age (under or over 30 years old). Based on these previous results, an exploratory factor analysis was carried out to test the applicability of the questionnaire to museums. After identifying the factors, the authors applied an ANOVA test to determine whether there are age-related differences between the factors related to robot acceptance in museums.FindingsThe authors developed a multidimensional instrument for measuring willingness to accept social robots in museum contexts. Willingness is determined by three factors: museum visitor experience (which is a factor specific to museums), empathy and personal engagement (which are both usually relevant in other tourist sectors as well). The younger individuals (under 30 years old) have a higher degree of acceptance than do visitors over 30.Originality/valueSocial robot use in museums is still very low, so the key factors for their acceptance have yet to be ascertained. The specific skills of social robots could prove to be a major draw for young people and contribute significantly to the future of museums.

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Using Gesture-Based Interfaces to Control Robots

Cited by 11 publications

References 5 publications

Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications

Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications

DARE: AI-based Diver Action Recognition System using Multi-Channel CNNs for AUV Supervision

Willingness to accept social robots in museums: an exploratory factor analysis according to visitor profile

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