探索机器人进化中学习机制的复杂性与设计方法
Robots Need Some Education: On the complexity of learning in evolutionary robotics
- 在机器人进化框架内设计适合的学习算法
- 揭示学习引入对进化过程的影响机制
- 适合关注进化机器人与学习融合的研究者
进化机器人学与机器人学习是两个旨在自动优化机器人设计的领域。两者的核心差异在于优化对象和时间尺度：进化机器人学使用进化计算技术演化机器人的形态或控制器（或二者），而机器人学习则采用各类学习技术优化给定形态下的控制器。时间尺度上，进化跨越多代，学习发生在个体生命周期内。将机器人学习融入进化机器人学需精心设计适配的学习算法，但引入学习的影响尚未被充分理解，可能导致复杂后果。本论文研究这些内在复杂性，提出若干适用于进化机器人背景的学习算法。
原文摘要 · Abstract (English)
Evolutionary Robotics and Robot Learning are two fields in robotics that aim to automatically optimize robot designs. The key difference between them lies in what is being optimized and the time scale involved. Evolutionary Robotics is a field that applies evolutionary computation techniques to evolve the morphologies or controllers, or both. Robot Learning, on the other hand, involves any learning technique aimed at optimizing a robot's controller in a given morphology. In terms of time scales, evolution occurs across multiple generations, whereas learning takes place within the `lifespan' of an individual robot. Integrating Robot Learning with Evolutionary Robotics requires the careful design of suitable learning algorithms in the context of evolutionary robotics. The effects of introducing learning into the evolutionary process are not well-understood and can thus be tricky. This thesis investigates these intricacies and presents several learning algorithms developed for an Evolutionary Robotics context.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。