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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References
Bohnstingl, T., Woźniak, S., Pantazi, A., Eleftheriou, E.: Online spatio-temporal learning in deep neural networks. In: IEEE Transactions on Neural Networks and Learning Systems (2022)
Chen, L., Sun, H., Zhao, W., Yu, T.: AI based gravity compensation algorithm and simulation of load end of robotic arm wrist force. Math. Probl. Eng. 2021(8), 1–11 (2021)
Chen, T.Y., Chiu, Y.C., Bi, N., Tsai, R.T.H.: Multi-modal chatbot in intelligent manufacturing. IEEE Access 9, 82118–82129 (2021)
Chen, Y., Qu, H., Zhang, M., Wang, Y.: Deep spiking neural network with neural oscillation and spike-phase information. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 7073–7080 (2021)
Deng, S., Li, Y., Zhang, S., Gu, S.: Temporal efficient training of spiking neural network via gradient re-weighting. In: International Conference on Learning Representations (2021)
Hagenaars, J., Paredes-Vallés, F., De Croon, G.: Self-supervised learning of event-based optical flow with spiking neural networks. In: Advances in Neural Information Processing Systems. vol. 34 (2021)
Jeong, J.H., Shim, K.H., Kim, D.J., Lee, S.W.: Brain-controlled robotic arm system based on multi-directional CNN-BiLSTM network using EEG signals. IEEE Trans. Neural Syst. Rehabil. Eng. 28(5), 1226–1238 (2020)
Jie, L., Sen, T., Ghani, N.M., Abas, M.F.: Automatic control of color sorting and pick/place of a 6- DOF robot arm. J. Européen des Systèmes Automatisés 54, 435–443 (2021)
Kamal, N., Singh, J.: A highly scalable junctionless fet leaky integrate-and-fire neuron for spiking neural networks. IEEE Trans. Electron Devices 68(4), 1633–1638 (2021)
Kiran, J.S., Prabhu, S.: Robot nano spray painting-a review. In: IOP Conference Series: Materials Science and Engineering. vol. 912, p. 032044. IOP Publishing (2020)
Koo, M., Srinivasan, G., Shim, Y., Roy, K.: sBSNN: stochastic-bits enabled binary spiking neural network with on-chip learning for energy efficient neuromorphic computing at the edge. IEEE Trans. Circuits Syst. I Regul. Pap. 67(8), 2546–2555 (2020)
Lăzăroiu, G., Andronie, M., Iatagan, M., Geamănu, M., Ştefănescu, R., Dijmărescu, I.: Deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms in the internet of manufacturing things. ISPRS Int. J. Geo Inf. 11(5), 277 (2022)
Lee, C., Sarwar, S.S., Panda, P., Srinivasan, G., Roy, K.: Enabling spike-based backpropagation for training deep neural network architectures. Front. Neurosci. 14, 119 (2020)
Li, Y., et al.: One transistor one electrolyte-gated transistor based spiking neural network for power-efficient neuromorphic computing system. Adv. Func. Mater. 31(26), 2100042 (2021)
Li, Y., Guo, Y., Zhang, S., Deng, S., Hai, Y., Gu, S.: Differentiable spike: rethinking gradient-descent for training spiking neural networks. Adv. Neural. Inf. Process. Syst. 34, 23426–23439 (2021)
Neftci, E.O., Mostafa, H., Zenke, F.: Surrogate gradient learning in spiking neural networks: bringing the power of gradient-based optimization to spiking neural networks. IEEE Signal Process. Mag. 36(6), 51–63 (2019)
Panda, P., Aketi, S.A., Roy, K.: Toward scalable, efficient, and accurate deep spiking neural networks with backward residual connections, stochastic softmax, and hybridization. Front. Neurosci. 14, 653 (2020)
Park, K.B., Choi, S.H., Lee, J.Y., Ghasemi, Y., Mohammed, M., Jeong, H.: Hands-free human-robot interaction using multimodal gestures and deep learning in wearable mixed reality. IEEE Access 9, 55448–55464 (2021)
Paudel, B.R., Itani, A., Tragoudas, S.: Resiliency of SNN on black-box adversarial attacks. In: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 799–806. IEEE (2021)
Skatchkovsky, N., Jang, H., Simeone, O.: Spiking neural networks-part II: detecting spatio-temporal patterns. IEEE Commun. Lett. 25(6), 1741–1745 (2021)
Sreekar, C., Sindhu, V., Bhuvaneshwaran, S., Bose, S.R., Kumar, V.S.: Positioning the 5-DOF robotic arm using single stage deep CNN model. In: 2021 Seventh International conference on Bio Signals, Images, and Instrumentation (ICBSII), pp. 1–6. IEEE (2021)
Yang, J.Q., et al.: Leaky integrate-and-fire neurons based on perovskite memristor for spiking neural networks. Nano Energy 74, 104828 (2020)
Zenke, F., Vogels, T.P.: The remarkable robustness of surrogate gradient learning for instilling complex function in spiking neural networks. Neural Comput. 33(4), 899–925 (2021)
Zuo, C., et al.: Deep learning in optical metrology: a review. Light Sci. Appl. 11(1), 1–54 (2022)
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This work was supported in part by the Key-Area Research and Development Program of Guangzhou (202007030004).
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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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