Conditional Generative Adversarial Network-based Finger Position Estimation for Controlling Multi-Degrees-of-Freedom Myoelectric Prosthetic Hands

Jiang, Hongmei; Yamanoi, Yusuke; Kuroda, Yuki; Chen, Peiji; Togo, Shunta; Jiang, Yinlai; Yokoi, Hiroshi

doi:10.1109/cbs55922.2023.10115366

2022 IEEE International Conference on Cyborg and Bionic Systems (CBS) 2023

DOI: 10.1109/cbs55922.2023.10115366

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Conditional Generative Adversarial Network-based Finger Position Estimation for Controlling Multi-Degrees-of-Freedom Myoelectric Prosthetic Hands

Hongmei Jiang

Yusuke Yamanoi

Yuki Kuroda

et al.

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2024

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Robust Learning from Demonstration Based on GANs and Affine Transformation

An,

Wu,

Shangguan

et al. 2024

Applied Sciences

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Collaborative robots face barriers to widespread adoption due to the complexity of programming them to achieve human-like movement. Learning from demonstration (LfD) has emerged as a crucial solution, allowing robots to learn tasks directly from expert demonstrations, offering versatility and an intuitive programming approach. However, many existing LfD methods encounter issues such as convergence failure and lack of generalization ability. In this paper, we propose: (1) a generative adversarial network (GAN)-based model with multilayer perceptron (MLP) architecture, coupled with a novel loss function designed to mitigate convergence issues; (2) an affine transformation-based generalization method aimed at enhancing LfD tasks by improving their generalization performance; (3) a data preprocessing method tailored to facilitate deployment on robotics platforms. We conduct experiments on a UR5 robotic platform tasked with handwritten digit recognition. Our results demonstrate that our proposed method significantly accelerates generation speed, achieving a remarkable processing time of 23 ms, which is five times faster than movement primitives (MPs), while preserving key features from demonstrations. This leads to outstanding convergence and generalization performance.

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Robust Learning from Demonstration Based on GANs and Affine Transformation

An,

Wu,

Shangguan

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

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Conditional Generative Adversarial Network-based Finger Position Estimation for Controlling Multi-Degrees-of-Freedom Myoelectric Prosthetic Hands

Cited by 1 publication

References 8 publications

Robust Learning from Demonstration Based on GANs and Affine Transformation

Robust Learning from Demonstration Based on GANs and Affine Transformation

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