Lamarck’s Revenge
Authors:   , Luo Jie 
Published: May 2024

The field of Evolutionary Robotics (ER) aims to apply evolutionary computation techniques to evolve the overall design, or controllers, or both, of real and simulated autonomous robots. This field dates back to the early 1990s. Unlike classical engineering approaches, based on mathematics and physics, ER is inspired by a less understood mechanism: biological evolution. One of the most enduring controversies in evolutionary biology is \textbf{Lamarckism}, which asserts that adaptations acquired or learned by an individual during its lifetime can be passed onto its offspring \cite{Conway1946}. Although it remains uncertain whether this Lamarckian idea occurs in nature, in the field of artificial intelligence, we have the ability to make it a reality.

The main goal of this research was to investigate Lamarckism in evolutionary robotics which presents a rich tapestry of insights into the interplay between learning, evolution, and environments in robotic systems. This discussion aims to synthesize the key findings across these stages and offer a broader perspective on the implications for the field.

This PhD research began by scrutinizing the "Triangle of Life" framework, with a specific focus on the additional learning loop, to determine its actual impact on the conjoint evolution of the brain and body. Given that Lamarckism inherently involves learning, the initial step involves validating the effectiveness of this supplementary learning loop. The study from the first stage emphasizes the significant advantages of integrating a learning loop into the evolutionary process.

Following the confirmation of the learning effects, the subsequent goal was to establish the feasibility of a Lamarckian system. To construct a robust Lamarckian framework, a systematic exploration of various aspects is undertaken. This includes the evaluation of diverse controller and learner combinations, an in-depth investigation into different reproduction methods, and genotype-to-phenotype mapping techniques.

After setting up an efficient Lamarckian system, we compared it with a Darwinian system and delved into the reasons why the Lamarckian system is superior. We provided evidence supporting the efficiency and efficacy of Lamarckian evolution and the existence of morphological intelligence.

In the last stage, we explored Lamarckism in dynamic environments. To validate the system's efficacy, the experiments extend beyond simulated environments and delve into real-world scenarios. The study highlights the Lamarckian system's effectiveness in dynamic environments, showcasing the tradeoff between adaptability and overfitting.

In summary, Lamarckian system emerges as a most complex yet feasible system in evolutionary robotics, validated through thorough exploration. The research contributions, ranging from the theoretical insights within the "Triangle of Life" framework to the practical implications, collectively contribute to the advancement of evolutionary robotics. This comprehensive framework holds the promise of evolving robust robots capable of adapting to unpredictable critical environments, thereby having the potential to help humans explore a new universe. The multifaceted exploration undertaken in this PhD research lays the groundwork for continued advancements and innovations in the realm of evolutionary robotics.