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On learning navigation behaviors for small mobile robots with reservoir computing architectures.

Eric Aislan Antonelo, Benjamin Schrauwen

IEEE Transactions on Neural Networks and Learning Systems

|March 21, 2015

Summary

This study introduces a reservoir computing (RC) framework for mobile robot navigation in unknown environments. The method efficiently trains neural networks to learn diverse navigation behaviors using sensory-motor sequences.

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Area of Science:

Robotics
Machine Learning
Artificial Intelligence

Background:

Mobile robots require robust navigation strategies for unknown and partially observable environments.
Traditional recurrent neural network training is computationally intensive.
Reservoir computing (RC) offers an efficient alternative by fixing the recurrent network (reservoir) and training only the output layer.

Purpose of the Study:

To propose a general reservoir computing (RC) learning framework for mobile robot navigation.
To enable learning of multiple navigation behaviors in simple and complex unknown environments.
To demonstrate how dynamic robot behaviors can be embedded and discriminated in the high-dimensional reservoir space.

Main Methods:

A general RC learning framework is proposed, leveraging the concept of navigation attractors within the reservoir's high-dimensional space.
Three distinct learning approaches are presented: supervised learning from examples, reward-based goal-directed learning, and hierarchical supervised learning for complex behaviors.
The framework utilizes the linear discriminability of sensory-motor sequences in the nonlinear dynamic reservoir space.

Main Results:

The RC framework successfully learns navigation behaviors for mobile robots in various environments.
Multiple behaviors can be learned and discriminated due to the high-dimensional embedding capabilities of the reservoir.
The proposed approaches demonstrate effective learning of both simple and complex goal-directed navigation tasks.

Conclusions:

Reservoir computing provides an efficient and effective framework for learning complex navigation behaviors in mobile robots.
The ability to embed and linearly discriminate dynamic behaviors in the reservoir's state space is key to learning multiple navigation strategies.
The proposed hierarchical approach enables sophisticated goal-directed navigation in challenging, partially observable environments.