Self-organized adaptation of a simple neural circuit enables complex robot behaviour

TL;DR

This paper presents a novel chaos control strategy for simple neural circuits that enables autonomous robots to rapidly adapt, learn, and generate complex, versatile behaviors across various situations.

Contribution

It introduces chaos control as a method for self-organizing complex behaviors in robots using a simple neural circuit with adaptive capabilities.

Findings

Generated 11 basic behaviors and their combinations.

Control signal adapts quickly and reversibly to new situations.

Enabled learning and long-term storage of behaviors.

Abstract

Controlling sensori-motor systems in higher animals or complex robots is a challenging combinatorial problem, because many sensory signals need to be simultaneously coordinated into a broad behavioural spectrum. To rapidly interact with the environment, this control needs to be fast and adaptive. Current robotic solutions operate with limited autonomy and are mostly restricted to few behavioural patterns. Here we introduce chaos control as a new strategy to generate complex behaviour of an autonomous robot. In the presented system, 18 sensors drive 18 motors via a simple neural control circuit, thereby generating 11 basic behavioural patterns (e.g., orienting, taxis, self-protection, various gaits) and their combinations. The control signal quickly and reversibly adapts to new situations and additionally enables learning and synaptic long-term storage of behaviourally useful motor responses. Thus, such neural control provides a powerful yet simple way to self-organize versatile behaviours in autonomous agents with many degrees of freedom.