This study explores the integration of Lamarckian system into evolutionary robotics (ER), comparing it with the traditional Darwinian model across various environments. By adopting Lamarckian principles, where robots inherit learned traits, alongside Darwinian learning without inheritance, we investigate adaptation in dynamic settings. Our research, conducted in six distinct environmental setups, demonstrates that Lamarckian systems outperform Darwinian ones in adaptability and efficiency, particularly in challenging conditions. Our analysis highlights the critical role of the interplay between controller \& morphological evolution and environment adaptation, with parent-offspring similarities and newborn \&survivors before and after learning providing insights into the effectiveness of trait inheritance. Our findings suggest Lamarckian principles could significantly advance autonomous system design, highlighting the potential for more adaptable and robust robotic solutions in complex, real-world applications. These theoretical insights were validated using real physical robots, bridging the gap between simulation and practical application.

翻译：本研究探讨了将拉马克式系统融入进化机器人学（ER）的方法，并与传统达尔文式模型在不同环境中进行比较。通过采用拉马克式原理（机器人可继承习得特性）以及无继承的达尔文式学习，我们研究了动态环境下的适应性机制。在六种不同环境设置中进行的实验表明，拉马克式系统在适应性与效率方面均优于达尔文式系统，尤其在挑战性条件下表现突出。分析强调了控制器与形态进化及环境适应之间相互作用的关键作用，通过亲子代相似性以及学习前后新生个体与幸存者的对比，揭示了特性继承的有效性。研究结果表明，拉马克式原理可显著推进自主系统设计，展现了在复杂真实世界应用中实现更适应、更稳健机器人方案的潜力。这些理论见解已通过实体物理机器人验证，弥合了仿真与实际应用之间的鸿沟。