Physical reservoir computing with origami and its application to robotic crawling

Abstract

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.

A Hopf physical reservoir computer

“…The reservoir is also evaluated with emulation tasks. The nonlinear auto-regressive moving average (NARMA) time series is used to test whether the reservoir possesses adequate nonlinearity and long time lags 4 , 24 , 26 , 28 . These tasks show the multi-tasking capability of the reservoir.…”

“…( 13 ), is the target of the system. n is the order of NARMA task, , and 26 , 28 . u ( t ) is the continuous input that is used to force the Hopf PRC, which is a function of a three sinusoidal functions.…”

“…The echo state architecture of a reservoir allows the use of physical systems as reservoir computers, also known as physical reservoir computers (PRCs). Many physical systems have been shown to perform as PRCs, including an array of nonlinear mechanical oscillators 11 , 22 , 23 , soft robotic bodies 20 , 24 – 26 , tensegrity structures 21 , 27 , and origami structures 28 , 29 .…”

Leveraging plant physiological dynamics using physical reservoir computing

“…This paradigm entails using a high-dimensional, nonlinear dynamical (physical) system as a computational resource to solve a task. Examples encompass the control of mechanical systems by using a compliant robot body 14 , the processing of optical 15 and electrical 16 signals, or quantum reservoir computers 17 . In one particular case, a water bucket was used as a physical reservoir to build a liquid computer that could solve the XOR-problem 18 .…”

Self‐Organization of Remote Reservoirs: Transferring Computation to Spatially Distant Locations

“…Tanaka et al demonstrated a method for identifying the direction of a wind stream from the movement of a soft flapping wing measured by flexible strain sensors as PRC. [ 21 ] An increasing number of PRC applications has been recently proposed for many different soft robotic platforms, such as tensegrity structures, [ 22 ] quadruped robots, [ 23 ] pneumatically driven systems, [ 24,25 ] fish robots, [ 26 ] origami robots, [ 27 ] and so on. In these examples, because the computational load is mainly outsourced to the physical body, we can expect to see savings in information processing.…”