ArticlePatterns (New York, N.Y.)2022
Data-driven artificial and spiking neural networks for inverse kinematics in neurorobotics.
Article in Patterns (New York, N.Y.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Exploiting noise as a resource for computation and learning in spiking neural networks.Patterns (New York, N.Y.) · 2023Article
- Implementation of Kalman Filtering with Spiking Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Comparison of Graph Fitting and Sparse Deep Learning Model for Robot Pose Estimation.Sensors (Basel, Switzerland) · 2022Article
- Walking on the thin intersectional lines of disciplines.Patterns (New York, N.Y.) · 2022Article
- Adaptive control of a wheelchair mounted robotic arm with neuromorphically integrated velocity readings and online-learning.Frontiers in neuroscience · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.