Real-time gesture recognition using a humanoid robot with a deep neural architecture

Barros, Pablo; Parisi, German I.; Jirak, Doreen; Wermter, Stefan

doi:10.1109/humanoids.2014.7041431

Cited by 32 publications

(19 citation statements)

References 10 publications

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“…The existing robotic systems can only recognize a few, usually pre-defined, hand gestures. For instance, a system proposed by Xiao et al [117] can recognize a set of 12 communicative gestures and actions with objects, while a system based on a deep neural network can robustly recognize 5 gestures performed by different people [11]. More recently, Castillo et al [24] proposed a system able to recognize 14 different gestures (i.e., come toward, stop, greeting, pointing left/right).…”

Section: Can Embodied Agents Recognize Multimodal Cues Produced By Humentioning

confidence: 99%

Beyond robotic speech: mutual benefits to cognitive psychology and artificial intelligence from the study of multimodal communication

Grzyb¹,

Vigliocco²

2020

Preprint

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Language has predominately been studied as a unimodal phenomenon - as speech or text without much consideration of its physical and social context – this is true both in cognitive psychology/psycholinguistics as well as in artificial intelligence. However, in everyday life, language is most often used in face-to-face communication and in addition to structured speech it comprises a dynamic system of multiplex components such as gestures, eye gaze, mouth movements and prosodic modulation. Recently, cognitive scientists have started to realise the potential importance of multimodality for the understanding of human communication and its neural underpinnings; while AI scientists have begun to address how to integrate multimodality in order to improve communication between human and artificial embodied agent. We review here the existing literature on multimodal language learning and processing in humans and the literature on perception of artificial agents, their comprehension and production of multimodal cues and we discuss their main limitations. We conclude by arguing that by joining forces AI scientists can improve the effectiveness of human-machine interaction and increase the human-likeness and acceptance of embodied agents in society. In turn, computational models that generate language in artificial embodied agents constitute a unique research tool to investigate the underlying mechanisms that govern language processing and learning in humans.

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Section: Can Embodied Agents Recognize Multimodal Cues Produced By Humentioning

confidence: 99%

Beyond robotic speech: mutual benefits to cognitive psychology and artificial intelligence from the study of multimodal communication

Grzyb¹,

Vigliocco²

2020

Preprint

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“…Naturally, the platform also inspired other humanoid soccer teams, such as the WF Wolves [13], to improve upon their own robots. The NimbRo-OP, which was a prototype for the igus Humanoid Open Platform, has been successfully used in research for human-robot interaction research at the University of Hamburg [14]. We recently sold a set of printed parts to the University of Newcastle in Australia and await results of their work.…”

Section: Receptionmentioning

confidence: 99%

First International HARTING Open Source Prize Winner: The igus Humanoid Open Platform

Allgeuer

Ficht

Farazi

et al. 2017

RoboCup 2016: Robot World Cup XX

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The use of standard platforms in the field of humanoid robotics can lower the entry barrier for new research groups, and accelerate research by the facilitation of code sharing. Numerous humanoid standard platforms exist in the lower size ranges of up to 60 cm, but beyond that humanoid robots scale up quickly in weight and price, becoming less affordable and more difficult to operate, maintain and modify. The igus Humanoid Open Platform is an affordable, fully open-source platform for humanoid research. At 92 cm, the robot is capable of acting in an environment meant for humans, and is equipped with enough sensors, actuators and computing power to support researchers in many fields. The structure of the robot is entirely 3D printed, leading to a lightweight and visually appealing design. This paper covers the mechanical and electrical aspects of the robot, as well as the main features of the corresponding open-source ROS software. At RoboCup 2016, the platform was awarded the first International HARTING Open Source Prize.

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“…In previous approaches, the use of three channels was explored, and presented good results for hand posture [18] and motion recognition [23]. The concept behind this architecture is to make use of existing knowledge, here represented by a dif ferent channel, to diversify the input.…”

Section: Architecturementioning

confidence: 99%

Face expression recognition with a 2-channel Convolutional Neural Network

Hamester

Barros

Wermter

2015

2015 International Joint Conference on Neural Networks (IJCNN)

Self Cite

106

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A new architecture based on the Multi-channel Convolutional Neural Network (MCCNN) is proposed for rec ognizing facial expressions. Two hard-coded feature extractors are replaced by a single channel which is partially trained in an unsupervised fashion as a Convolutional Autoencoder (CAE). One additional channel that contains a standard CNN is left unchanged. Information from both channels converges in a fully connected layer and is then used for classification. We perform two distinct experiments on the JAFFE dataset (Ieave-one-out and ten-fold cross validation) to evaluate our architecture. Our comparison with the previous model that uses hard-coded Sobel features shows that an additional channel of information with unsupervised learning can significantly boost accuracy and re duce the overall training time. Furthermore, experimental results are compared with benchmarks from the literature showing that our method provides state-of-the-art recognition rates for facial expressions. Our method outperforms previously published methods that used hand-crafted features by a large margin.

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Real-time gesture recognition using a humanoid robot with a deep neural architecture

Cited by 32 publications

References 10 publications

Beyond robotic speech: mutual benefits to cognitive psychology and artificial intelligence from the study of multimodal communication

Beyond robotic speech: mutual benefits to cognitive psychology and artificial intelligence from the study of multimodal communication

First International HARTING Open Source Prize Winner: The igus Humanoid Open Platform

Face expression recognition with a 2-channel Convolutional Neural Network

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