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Energy-Efficient Robotic Arm Control Based on Differentiable Spiking Neural Networks

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Image and Graphics (ICIG 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14355))

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Abstract

Robotic arm control using deep neural networks (DNNs) has gained attention due to its high precision and flexibility, but requires significant computational resources and results in massive energy consumption. In contrast, spiking neural networks (SNNs) are energy-efficient, but their discretized activation function and gradient mismatch pose challenges. To address these issues, this paper proposes a hardware-independent control scheme for robotic arms based on differentiable SNNs (DSNNs). Our approach utilizes binary spike sequences generated by the DSNN to significantly reduce the cost of inference. The output of spiking neurons in DSNNs is refactored, enabling direct backpropagation training while gradually approaching a pulse function. In addition, a hand gesture dataset is created as the controlling input. Simulations and experiments on a virtual robotic arm platform demonstrate that our approach achieves high efficiency and accuracy in controlling the robotic arm with gestures, while consuming less energy compared to other methods.

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Acknowledgements

This work was supported in part by the Key-Area Research and Development Program of Guangzhou (202007030004).

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Correspondence to Jianhuang Lai .

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Wang, X., Tang, J., Lai, J. (2023). Energy-Efficient Robotic Arm Control Based on Differentiable Spiking Neural Networks. In: Lu, H., et al. Image and Graphics. ICIG 2023. Lecture Notes in Computer Science, vol 14355. Springer, Cham. https://doi.org/10.1007/978-3-031-46305-1_19

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  • DOI: https://doi.org/10.1007/978-3-031-46305-1_19

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