Greta Brown

A key objective in artificial intelligence and robotics is to develop systems capable of autonomous decision-making and adaptation to dynamic environments. Evolutionary robotics, a sub-field of robotics, aims to achieve this by drawing inspiration from natural evolution and applying principles of gene selection and variation within robots to enable adaptation. Embodied evolution, a subset of evolutionary robotics, distinguishes itself from other evolutionary algorithms by distributing control among the robots rather than centrally, allowing for asynchronous, autonomous, and continuous evolution. Reinforcement learning is a method for agents to learn autonomously based on trial-and-error learning. Researchers suggest that combining reinforcement learning with embodied evolution may enhance robotic collectives’ adaptability in dynamic settings, an idea inspired by studies of behavioral plasticity and social learning in animal populations. This research takes an essential step toward testing these ideas in physical systems rather than simulations, which are commonly used but fail to address many real-world complexities. Using TurtleBot3 robots equipped with Raspberry Pi 4 and 5 modules, this work involved constructing the robots, equipping them with hardware, and setting up ROS 2 as a foundational environment for implementing reinforcement learning and embodied evolution. While the theoretical algorithms remain to be tested, this groundwork is crucial for transitioning these concepts from simulation to reality. The emphasis on physical deployment ensures that the challenges and nuances of real-world applications, often overlooked in simulation, are addressed, paving the way for adaptive robotic systems with greater practical utility.