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.

本研究探讨了拉马克系统在进化机器人技术中的整合，并与传统的达尔文模型在各种环境中进行比较。通过采用拉马克原理，在机器人继承学习特性的基础上，结合没有继承的达尔文学习，在动态环境下研究适应性。我们的研究在六种不同的环境设置中进行，证明了拉马克系统在适应性和效率方面优于达尔文系统，特别是在具有挑战性的条件下。我们的分析突出了控制器和形态进化与环境适应之间的相互作用的关键作用，父子相似性以及学习前后的新生儿和幸存者提供了对特性继承有效性的深入理解。我们的研究结果表明，拉马克原理可以显著推进自治系统设计，在复杂的实际应用中提供更具适应性和强大的机器人解决方案的潜力。同时，我们使用实际物理机器人验证了这些理论洞察力，填补了仿真和实际应用之间的鸿沟。