AI Bibliography
AI Bibliography |
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Bhovad, P., & Li, S. (2021). Physical reservoir computing with origami and its application to robotic crawling. Scientific reports, 11(1), 1–18. |
| Resource type: Journal Article BibTeX citation key: Bhovad2021 View all bibliographic details  |
Categories: Artificial Intelligence, Biological Science, Cognitive Science, Complexity Science, Computer Science, Decision Theory, Engineering, General, Neuroscience Subcategories: Autonomous systems, Chaos theory, Decision making, Deep learning, Edge AI, Machine intelligence, Machine learning, Neural nets, Neurosymbolic, Synthetic intelligence Creators: Bhovad, Li Publisher: Collection: Scientific reports |
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A new paradigm called physical reservoir computing has recently emerged, where the nonlinear dynamics of high-dimensional and fixed physical systems are harnessed as a computational resource to achieve complex tasks. Via extensive simulations based on a dynamic truss-frame model, this study shows that an origami structure can perform as a dynamic reservoir with sufficient computing power to emulate high-order nonlinear systems, generate stable limit cycles, and modulate outputs according to dynamic inputs. This study also uncovers the linkages between the origami reservoir’s physical designs and its computing power, offering a guideline to optimize the computing performance. Comprehensive parametric studies show that selecting optimal feedback crease distribution and fine-tuning the underlying origami folding designs are the most effective approach to improve computing performance. Furthermore, this study shows how origami’s physical reservoir computing power can apply to soft robotic control problems by a case study of earthworm-like peristaltic crawling without traditional controllers. These results can pave the way for origami-based robots with embodied mechanical intelligence.
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