Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics

Alex Volinski, Yuval Zaidel, Albert Shalumov, Travis DeWolf, Lazar Supic, Elishai Ezra Tsur

نتاج البحث: نشر في مجلةمقالةمراجعة النظراء


Inverse kinematics is fundamental for computational motion planning. It is used to derive an appropriate state in a robot's configuration space, given a target position in task space. In this work, we investigate the performance of fully connected and residual artificial neural networks as well as recurrent, learning-based, and deep spiking neural networks for conventional and geometrically constrained inverse kinematics. We show that while highly parameterized data-driven neural networks with tens to hundreds of thousands of parameters exhibit sub-ms inference time and sub-mm accuracy, learning-based spiking architectures can provide reasonably good results with merely a few thousand neurons. Moreover, we show that spiking neural networks can perform well in geometrically constrained task space, even when configured to an energy-conserved spiking rate, demonstrating their robustness. Neural networks were evaluated on NVIDIA's Xavier and Intel's neuromorphic Loihi chip.

اللغة الأصليةالإنجليزيّة
رقم المقال100391
الصفحات (من إلى)100391
عدد الصفحات1
مستوى الصوت3
رقم الإصدار1
المعرِّفات الرقمية للأشياء
حالة النشرنُشِر - 14 يناير 2022

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