Online Robot Teaching With Natural Human–Robot Interaction

Du, Guanglong; Chen, Mingxuan; Liu, Caibing; Zhang, Bo; Zhang, Ping

doi:10.1109/tie.2018.2823667

Cited by 91 publications

(46 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In [32], the authors present an online robot teaching method that fuses speech and gesture information using text. Kinect V2 localizes the position of hands in the scene, while their orientation is measured by an inertial measurement unit.…”

Section: Gesture Detection In Human-robot Interactionmentioning

confidence: 99%

A real-time human-robot interaction framework with robust background invariant hand gesture detection

Mazhar

Navarro

Ramdani

et al. 2019

Robotics and Computer-Integrated Manufacturing

104

View full text Add to dashboard Cite

In the light of factories of the future, to ensure productive and safe interaction between robot and human coworkers, it is imperative that the robot extracts the essential information of the coworker. We address this by designing a reliable framework for real-time safe human-robot collaboration, using static hand gestures and 3D skeleton extraction. OpenPose library is integrated with Microsoft Kinect V2, to obtain a 3D estimation of the human skeleton. With the help of 10 volunteers, we recorded an image dataset of alphanumeric static hand gestures, taken from the American Sign Language. We named our dataset OpenSign and released it to the community for benchmarking. Inception V3 convolutional

show abstract

Section: Gesture Detection In Human-robot Interactionmentioning

confidence: 99%

A real-time human-robot interaction framework with robust background invariant hand gesture detection

Mazhar

Navarro

Ramdani

et al. 2019

Robotics and Computer-Integrated Manufacturing

104

View full text Add to dashboard Cite

show abstract

“…The end-effect inverse kinematics decomposition method is adopted to conduct feature decomposition and attitude simulation for the control structure of college English teaching robots (Sun and Pan, 2017;Du et al, 2018), then the lateral deflection transition probability of robotic attitudes is:…”

Section: Control Optimisation Of College English Teaching Robotsmentioning

confidence: 99%

College English Teaching Mode Based on Intelligent Robot

Wang

2019

IJCEELL

View full text Add to dashboard Cite

Using the traditional method to design college English teaching mode, normally the students' satisfaction is not high, which leads to the situation where the students' performance is not ideal. Therefore, a research method of college English teaching mode based on intelligent robots is proposed. Firstly, in order to realise the stable control of the intelligent robot in college English teaching, a motion planning model of the intelligent robot in college English teaching is constructed. Secondly, the control design of college English teaching machine is optimised. A specific improvement method to the college English teaching mode based on intelligent robots is proposed. Finally, the college English teaching mode based on intelligent robot is realised. According to the experimental results, the proposed method is significantly superior to the traditional method in terms of enhancement effect of students' satisfaction and performance, indicating that the proposed method has high application value and certain reference value.

show abstract

“…Its features are extracted for classification by Term Frequency-Inverse Document Frequency (TF-IDF), detailed in [28]. The maximum entropy model is adopted as the classifier to classify the gestures and speech into the corresponding instruction, illustrated in [29]. Table 1 shows some examples of mapping natural interaction instructions into robot commands.…”

Section: Multimodal Instruction Generationmentioning

confidence: 99%

A Novel Natural Mobile Human-Machine Interaction Method With Augmented Reality

Zhang

et al. 2019

IEEE Access

Self Cite

View full text Add to dashboard Cite

This paper proposes a novel teleoperation method that allows users to guide robots hand by hand along with speech. In this method, the virtual robot modeled according to the remote real robot is projected into the real local environment to form a 3D operation interface. In this case, users can directly interact with virtual objects by their hands. Furthermore, since the Leap Motion is attached to the augmented reality (AR) glasses, the operation space is greatly extended. Therefore, users can observe the virtual robot from an arbitrary angle without blind angle in such a mobile pattern, which enhances the users' interactive immersion and provides more natural human-machine interaction. To improve the accuracy of the measurement, an unscented Kalman filter (UKF) and an improved particle filter (IPF) are used to estimate the position and orientation of the hand, respectively. Furthermore, Term Frequency-Inverse Document Frequency (TF-IDF) and maximum entropy model are adopted to recognize the speech and gestures instructions of the user. The proposed method is compared with the three human-machine methods on various experiments. The results verified the effectiveness of the proposed method. INDEX TERMS Teleoperation, augmented reality, hand by hand, unscented Kalman filter, term frequency-inverse document frequency.

show abstract

Online Robot Teaching With Natural Human–Robot Interaction

Cited by 91 publications

References 39 publications

A real-time human-robot interaction framework with robust background invariant hand gesture detection

A real-time human-robot interaction framework with robust background invariant hand gesture detection

College English Teaching Mode Based on Intelligent Robot

A Novel Natural Mobile Human-Machine Interaction Method With Augmented Reality

Contact Info

Product

Resources

About