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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Re-Purposing a Modular Origami Manipulator Into an Adaptive Physical Computer for Machine Learning and Robotic

Jun Wang¹, Suyi Li¹

¹Department of Mechanical Engineering, Virginia Tech, Blacksburg, VA, 24060, USA.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)

|September 14, 2025

Summary

This summary is machine-generated.

This study shows mechanical design significantly impacts physical computing in robots. Optimizing configurations enhances performance for tasks like time series emulation and perception.

Keywords:

mechanical intelligence perception physical computing soft robots

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Area of Science:

Robotics and Mechanical Engineering
Computational Science
Materials Science

Background:

Physical computing offers an alternative to traditional CMOS computers for intelligent tasks in mechanical systems.
A systematic understanding of how mechanical design influences physical computing performance is lacking.

Purpose of the Study:

To investigate the relationship between mechanical design and physical computing performance.
To evaluate the computing capacity of an adaptive physical reservoir based on its configuration.
To demonstrate the potential for embodied intelligence in soft robots and functional materials.

Main Methods:

Repurposed an origami-inspired modular robotic manipulator as an adaptive physical reservoir.
Systematically evaluated computing capacity with varying physical configurations, input setups, and tasks.
Utilized the NARMA benchmark for time series emulation and analyzed nodal dynamics for perception capabilities.
Integrated shape memory alloy (SMA) actuation for robotic operations.

Main Results:

Time series emulation performance directly correlates with the Peak Similarity Index (PSI), reflecting reservoir dynamics.
The adaptive reservoir demonstrated perception by extracting payload weight and orientation from intrinsic dynamics.
Information extraction capability is measurable via spatial correlation between nodal dynamics.
SMA actuation enabled practical robotic operations by exploiting embodied computing power.

Conclusions:

A strategic framework for harvesting computing power from soft robots and functional materials was established.
Design parameters and input selection can be configured based on specific computing task requirements.
This approach enables next-generation embodied intelligence in bio-inspired materials and robotic systems.