Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment

Area of Science:

• Robotics and Artificial Intelligence
• Evolutionary Computation
• Bio-inspired Systems

Background:

• Biological systems exhibit complex adaptive behaviors driven by gene regulatory networks (GRNs) and signal transduction.
• Existing robotic controllers often lack the dynamic adaptability seen in biological organisms.
• Simulating evolutionary processes in artificial systems is key to understanding emergent collective behaviors.

Purpose of the Study:

• To develop and evaluate a novel bio-inspired robot controller integrating an artificial genome with an agent-based system.
• To investigate the evolution of adaptive behaviors, such as resource acquisition, in simulated robot swarms.
• To demonstrate how bio-inspired evolutionary mechanisms can drive collective behavior in artificial agents.

Main Methods:

• Developed a controller where an artificial genome encodes a gene regulatory network (GRN) activated by environmental cues.
• Implemented an agent-based system to mimic biological active regulatory and signal transduction pathways.
• Utilized A-life environment simulations to test swarm robot adaptation and collective behavior evolution.

Main Results:

• The bio-inspired controller demonstrated adaptive behaviors, including preying in response to food scarcity.
• Separating static (genome) and dynamic (agent-based) GRN components enhanced general adaptive behavior.
• Simulations confirmed that collective behavior in robot swarms can evolve through multi-level bio-inspired processes.

Conclusions:

• The developed platform effectively simulates the evolution of adaptive and collective behaviors in robots.
• Bio-inspired evolutionary processes, acting from gene to population level, are crucial for emergent robotic intelligence.
• This approach offers a promising framework for creating more adaptable and robust artificial systems.