# Physical Reservoir Computing with Origami and its Application to Robotic Crawling

arXiv: https://arxiv.org/abs/2101.08348 · HTML: https://mlsanity.com/paper/2101.08348/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jan 2021.

## Built on by (most cited first, 8 of 8)

- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)
- [Building Intelligence in the Mechanical Domain -- Harvesting the Reservoir Computing Power in Origami to Achieve Information Perception Tasks](https://mlsanity.com/paper/2302.05517/) (2023)
- [Multifunctional physical reservoir computing in soft tensegrity robots](https://mlsanity.com/paper/2507.21496/) (2025)
- [Uncovering multifunctional mechano-intelligence in and through phononic metastructures harnessing physical reservoir computing](https://mlsanity.com/paper/2305.19354/) (2023)
- [Predicting unobserved climate time series data at distant areas via spatial correlation using reservoir computing](https://mlsanity.com/paper/2406.03061/) (2024)
- [Exoplanetary atmospheres retrieval via a quantum extreme learning machine](https://mlsanity.com/paper/2509.03617/) (2025)
- [OpenPRC: A Unified Open-Source Framework for Physics-to-Task Evaluation in Physical Reservoir Computing](https://mlsanity.com/paper/2604.07423/) (2026)
- [On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification](https://mlsanity.com/paper/2604.21602/) (2026)

## Abstract (arXiv)

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.