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。

动态环境中的进化机器人改进了拉马克遗传

Lamarckian Inheritance Improves Robot Evolution in Dynamic Environments

Evolutionary robotics offers a powerful framework for designing and evolving
robot morphologies, particularly in the context of modular robots. However, the
role of query mechanisms during the genotype-to-phenotype mapping process has
been largely overlooked. This research addresses this gap by conducting a
comparative analysis of query mechanisms in the brain-body co-evolution of
modular robots. Using two different query mechanisms, Breadth-First Search
(BFS) and Random Query, within the context of evolving robot morphologies using
CPPNs and robot controllers using tensors, and testing them in two evolutionary
frameworks, Lamarckian and Darwinian systems, this study investigates their
influence on evolutionary outcomes and performance. The findings demonstrate
the impact of the two query mechanisms on the evolution and performance of
modular robot bodies, including morphological intelligence, diversity, and
morphological traits. This study suggests that BFS is both more effective and
efficient in producing highly performing robots. It also reveals that
initially, robot diversity was higher with BFS compared to Random Query, but in
the Lamarckian system, it declines faster, converging to superior designs,
while in the Darwinian system, BFS led to higher end-process diversity.

进化机器人学中查询机制对模块化机器人的演化和性能有着重要影响，研究发现 BFS 对于产生高性能机器人更为有效和高效。同时，该研究还揭示了不同演化系统下查询机制对机器人多样性和形态特征的影响。

通过广度优先搜索和随机查询探索机器人形态空间

Exploring Robot Morphology Spaces through Breadth-First Search and  Random Query

The ongoing deep learning revolution has allowed computers to outclass humans
in various games and perceive features imperceptible to humans during
classification tasks. Current machine learning techniques have clearly
distinguished themselves in specialized tasks. However, we have yet to see
robots capable of performing multiple tasks at an expert level. Most work in
this field is focused on the development of more sophisticated learning
algorithms for a robot's controller given a largely static and presupposed
robotic design. By focusing on the development of robotic bodies, rather than
neural controllers, I have discovered that robots can be designed such that
they overcome many of the current pitfalls encountered by neural controllers in
multitask settings. Through this discovery, I also present novel metrics to
explicitly measure the learning ability of a robotic design and its resistance
to common problems such as catastrophic interference.
Traditionally, the physical robot design requires human engineers to plan
every aspect of the system, which is expensive and often relies on human
intuition. In contrast, within the field of evolutionary robotics, evolutionary
algorithms are used to automatically create optimized designs, however, such
designs are often still limited in their ability to perform in a multitask
setting. The metrics created and presented here give a novel path to automated
design that allow evolved robots to synergize with their controller to improve
the computational efficiency of their learning while overcoming catastrophic
interference.
Overall, this dissertation intimates the ability to automatically design
robots that are more general purpose than current robots and that can perform
various tasks while requiring less computation.

通过设计机器人体形，可以在多任务场景下克服神经控制器所遇到的许多问题，从而实现设计更通用、需要更少计算力的机器人。