A Real-Time Upper-Body Robot Imitation System

Zhang, Zhijun; Niu, Yaru; Kong, Lingdong; Lin, Shuyang; Wang, Hao

doi:10.5430/ijrc.v2n1p49

Cited by 9 publications

(3 citation statements)

References 15 publications

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“…This process is effective but its complexity requires high computational load. Alternatively, direct kinematics adapts captured angles to the robot [36,38]. As it is a more straightforward and computationally tractable method, this last technique has been used in the system presented here.…”

Section: Mapping: Translating Human Motion To Robot Motionmentioning

confidence: 99%

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Modeling and evaluating beat gestures for social robots

Zabala

Rodriguez

Martínez-Otzeta

et al. 2021

Multimed Tools Appl

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Natural gestures are a desirable feature for a humanoid robot, as they are presumed to elicit a more comfortable interaction in people. With this aim in mind, we present in this paper a system to develop a natural talking gesture generation behavior. A Generative Adversarial Network (GAN) produces novel beat gestures from the data captured from recordings of human talking. The data is obtained without the need for any kind of wearable, as a motion capture system properly estimates the position of the limbs/joints involved in human expressive talking behavior. After testing in a Pepper robot, it is shown that the system is able to generate natural gestures during large talking periods without becoming repetitive. This approach is computationally more demanding than previous work, therefore a comparison is made in order to evaluate the improvements. This comparison is made by calculating some common measures about the end effectors’ trajectories (jerk and path lengths) and complemented by the Fréchet Gesture Distance (FGD) that aims to measure the fidelity of the generated gestures with respect to the provided ones. Results show that the described system is able to learn natural gestures just by observation and improves the one developed with a simpler motion capture system. The quantitative results are sustained by questionnaire based human evaluation.

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Section: Mapping: Translating Human Motion To Robot Motionmentioning

confidence: 99%

“…The literature reveals different approaches to calculate the robot arm joint positions [12,38]. This mapping process depends upon the robot's degrees of freedom and joints range.…”

Section: Arms Mappingmentioning

confidence: 99%

Modeling and evaluating beat gestures for social robots

Zabala

Rodriguez

Martínez-Otzeta

et al. 2021

Multimed Tools Appl

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“…This mapping process can be done by inverse kinematics [1], calculating the necessary joint positions given a desired end effector's pose as in [29]. Alternatively, we adopt the direct kinematics option that straightforwardly adapts the captured arms and head angles to the robot joints [48] [35].…”

Section: Experimental Baselinementioning

confidence: 99%

Quantitative analysis of robot gesticulation behavior

Zabala,

Rodriguez,

Martínez-Otzeta

et al. 2020

Preprint

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Social robot capabilities, such as talking gestures, are best produced using data driven approaches to avoid being repetitive and to show trustworthiness. However, there is a lack of robust quantitative methods that allow to compare such methods beyond visual evaluation. In this paper a quantitative analysis is performed that compares two Generative Adversarial Networks based gesture generation approaches. The aim is to measure characteristics such as fidelity to the original training data, but at the same time keep track of the degree of originality of the produced gestures. Principal Coordinate Analysis and procrustes statistics are performed and a new Fréchet Gesture Distance is proposed by adapting the Fréchet Inception Distance to gestures. These three techniques are taken together to asses the fidelity/originality of the generated gestures.

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