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Learning of Central Pattern Generator Coordination in Robot Drawing.

Payam Atoofi¹, Fred H Hamker¹, John Nassour¹

¹Artificial Intelligence, Computer Science, Chemnitz University of Technology, Chemnitz, Germany.

Frontiers in Neurorobotics

|August 8, 2018

Summary

This summary is machine-generated.

This study introduces a novel robot learning framework using Central Pattern Generators (CPGs) and optimization to acquire motor skills. The system effectively transfers learned motor coordination across different workspace areas for versatile task performance.

Keywords:

central pattern generator coordination transfer motor coordination robot drawing robot learning

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Area of Science:

Robotics
Machine Learning
Control Systems

Background:

Robots struggle to adapt learned motor skills to new environments.
Existing methods often focus on point-to-point tasks, limiting generalizability.
Motor coordination acquisition and transfer remain key challenges in robotics.

Purpose of the Study:

To develop a framework for robots to learn motor coordination for tasks like drawing straight lines.
To enable the transfer of acquired motor skills to different workspace regions.
To achieve generalized motor control beyond initial learning conditions.

Main Methods:

Utilizing a Central Pattern Generator (CPG) model for motor pattern generation.
Employing multi-objective optimization to adjust CPG parameters for motor coordination acquisition.
Implementing a Self-Organizing Map (SOM) and Inverse Distance Weighting for motor program transfer across the workspace.

Main Results:

The robot successfully acquired motor coordination for drawing straight lines in a specific workspace area.
The proposed framework enabled the transfer of this coordination to other workspace regions.
The robot demonstrated generalization capabilities, drawing lines from various points and orientations.

Conclusions:

The developed framework allows robots to learn and generalize motor tasks across their workspace.
This approach offers a distinct alternative to inverse kinematics for continuous motor control.
The method facilitates robust motor program transfer, enhancing robot adaptability and versatility.