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A Novel and Accurate BiLSTM Configuration Controller for Modular Soft Robots with Module Number Adaptability.

Zixi Chen¹, Matteo Bernabei¹, Vanessa Mainardi¹

¹Biorobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy.

|December 9, 2024

Summary

This study presents a new data collection strategy and a bidirectional long short-term memory (biLSTM) controller for modular soft robots (MSRs). The approach enables adaptable control for MSRs with changing module numbers.

Keywords:

bidirectional LSTM configuration control data-driven control modular soft robot

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Area of Science:

Robotics
Artificial Intelligence
Control Systems

Background:

Modular soft robots (MSRs) offer enhanced capabilities over single-module designs but present control complexities.
Accurate control strategies are crucial for realizing the full potential of MSRs.

Purpose of the Study:

To introduce a novel data collection strategy for MSRs.
To develop an adaptive controller for MSRs that can handle varying numbers of modules.
To validate the proposed methods through simulations and real-world experiments.

Main Methods:

A tailored data collection strategy for MSRs.
A bidirectional long short-term memory (biLSTM) configuration controller designed for adaptability.
Experimental validation using simulated cable-driven robots and real pneumatic robots.

Main Results:

The data collection method enables MSRs to explore a larger operational space.
The biLSTM controller effectively adapts to changes in the number of modules.
The proposed control strategy is robust across different MSR configurations.

Conclusions:

The developed data collection and biLSTM control strategy significantly enhance MSR performance and adaptability.
The controller's ability to adapt to varying module numbers is a key advancement for MSRs.
Future research directions include integrating planning methods and online learning components.