Lamarckian Inheritance Improves Robot Evolution in Dynamic Environments

Abstract

Nature-inspired methods, such as evolutionary computing and lifetime learning, have shown great promise in advancing autonomous robot design. However, the integration of evolution and learning for the joint optimization of robot morphologies and controllers remains underexplored, particularly in dynamic environments. This paper addresses this gap by investigating the effectiveness of Lamarckian inheritance (a mechanism that allows learned traits to be encoded into the genotype and passed to offspring) in improving robot evolution in nonstationary environments. We compare a Lamarckian system with a traditional Darwinian system, where learned traits are not inherited. Using simulated modular robots in six distinct environmental setups, we analyze the fitness progression, learning ability, and parent-offspring similarity within both systems. Our results demonstrate that the Lamarckian system consistently outperforms the Darwinian system, achieving up to 33% higher fitness in the most challenging conditions. The Lamarckian system also recovers more quickly from environmental changes, showing immediate fitness gains when shifting to complex terrains, whereas the Darwinian system adapts more slowly. Real-world tests validate the robustness of the Lamarckian approach, as the top-performing robots evolved in the most challenging environment exhibit the smallest reality gap. These findings highlight the potential of Lamarckian inheritance as a powerful tool for engineering adaptive robotic systems capable of maintaining high performance in dynamic environments.