Bipedal robots are gaining global recognition due to their potential applications and advancements in artificial intelligence, particularly through Deep Reinforcement Learning (DRL). While DRL has significantly advanced bipedal locomotion, the development of a unified framework capable of handling a wide range of tasks remains an ongoing challenge. This survey systematically categorises, compares, and analyses existing DRL frameworks for bipedal locomotion, organising them into end-to-end and hierarchical control schemes. End-to-end frameworks are evaluated based on their learning approaches, while hierarchical frameworks are examined in terms of layered structures that integrate learning-based or traditional model-based methods. We provide a detailed evaluation of the composition, strengths, limitations, and capabilities of each framework. Additionally, this survey identifies key research gaps and proposes future directions aimed at creating a more integrated and efficient framework for bipedal locomotion, with wide-ranging applications in real-world environments.

翻译：双足机器人因其广阔的应用前景及人工智能（尤其是深度强化学习）的进步而日益受到全球关注。尽管深度强化学习已显著推动了双足运动控制的发展，但构建能够处理广泛任务的统一框架仍是持续面临的挑战。本综述系统性地分类、比较和分析了现有的双足运动控制深度强化学习框架，将其归纳为端到端控制架构与分层控制架构两类。端到端框架根据其学习方法进行评估，而分层框架则通过整合基于学习或传统模型方法的层级结构进行剖析。我们对各类框架的构成、优势、局限性和功能进行了详细评估。此外，本文指出了该领域的关键研究空白，并提出了旨在构建更集成、高效的双足运动控制框架的未来研究方向，以促进其在现实环境中的广泛应用。