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.
| Original language | English |
|---|---|
| Article number | 100391 |
| Pages (from-to) | 100391 |
| Number of pages | 1 |
| Journal | Patterns |
| Volume | 3 |
| Issue number | 1 |
| DOIs | |
| State | Published - 14 Jan 2022 |
Bibliographical note
Funding Information:The authors thank the Applied Brain Research (ABR) team for the support; Intel Labs for granting us access to their neuromorphic cloud and technical support; and Andreea Danielescu and Timothy Shea from Accenture Labs for their insightful comments. This research was funded by Accenture Labs as part of Intel's INRC (Intel Neuromorphic Research Community) initiative and by the Open University of Israel research grant. E.E.T. A.V. Y.Z. and A.S. conceptualized the study; E.E.T. A.V. Y.Z. A.S. and T.D. developed the methodology; E.E.T. A.V. Y.Z. A.S. T.D. and L.S. performed the formal analysis; E.E.T. wrote the manuscript; E.E.T. A.V. Y.Z. A.S. T.D. and L.S. reviewed and edited the manuscript; E.E.T. supervised the study and acquired funding. The authors declare no competing interests.
Funding Information:
The authors thank the Applied Brain Research (ABR) team for the support; Intel Labs for granting us access to their neuromorphic cloud and technical support; and Andreea Danielescu and Timothy Shea from Accenture Labs for their insightful comments. This research was funded by Accenture Labs as part of Intel's INRC (Intel Neuromorphic Research Community) initiative and by the Open University of Israel research grant.
Publisher Copyright:
© 2021 The Author(s)
© 2021 The Author(s).
© 2021 The Author(s).
© 2021 The Author(s).
Keywords
- DSML 2: Proof-of-Concept: Data science output has been formulated, implemented, and tested for one domain/problem
- Intel Loihi
- NVIDIA Xavier
- artificial neural networks
- neural engineering framework
- neuromorphic engineering
- online learning
- redundancy resolution
- robotic arm
- spiking neural networks
- underdetermined systems