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.

翻译：持续的深度学习革命使计算机在各种游戏中超越人类，并在分类任务中感知人类难以察觉的特征。当前机器学习技术已在特定任务中展现出显著优势。然而，我们尚未见到能够以专家级水平执行多重任务的机器人。该领域多数研究聚焦于为机器人控制器开发更复杂的学习算法，而预设其机械设计基本固定不变。通过聚焦机器人本体而非神经控制器的发展，我发现可通过设计使机器人克服多任务环境中神经控制器常遇到的诸多缺陷。基于这一发现，我还提出了全新度量指标，用于显式评估机器人设计的学习能力及其对灾难性干扰等常见问题的抗性。传统上，物理机器人设计需要人类工程师规划系统的每个方面，这既成本高昂又常依赖直觉。相比之下，演化机器人学领域采用演化算法自动生成优化设计，但这类设计仍往往难以在多任务场景中有效运作。本文提出的度量指标为自动化设计开辟了新路径，使演化机器人能与其控制器协同作用，在提升学习计算效率的同时克服灾难性干扰。总体而言，本论文揭示了自动设计比现有机器人更具通用性、能以更低计算量执行多种任务的机器人的潜力。