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：双足机器人因其潜在应用价值及人工智能（特别是深度强化学习（DRL））的进步而日益受到全球关注。尽管DRL已显著推动了双足步态控制的发展，但构建能够处理广泛任务的统一框架仍是一个持续存在的挑战。本综述系统性地分类、比较并分析了现有的双足步态控制DRL框架，将其归纳为端到端与分层控制两种方案。端到端框架根据其学习方法进行评估，而分层框架则从整合基于学习或传统基于模型方法的分层结构角度进行考察。我们对每种框架的构成、优势、局限及性能进行了详细评估。此外，本综述指出了关键的研究空白，并提出了旨在为双足步态控制构建更集成、更高效框架的未来研究方向，以拓展其在现实环境中的广泛应用。