Bipedal robots are garnering increasing global attention due to their potential applications and advancements in artificial intelligence, particularly in Deep Reinforcement Learning (DRL). While DRL has driven significant progress in bipedal locomotion, developing a comprehensive and unified framework capable of adeptly performing a wide range of tasks remains a challenge. This survey systematically categorizes, compares, and summarizes existing DRL frameworks for bipedal locomotion, organizing them into end-to-end and hierarchical control schemes. End-to-end frameworks are assessed based on their learning approaches, whereas hierarchical frameworks are dissected into layers that utilize either learning-based methods or traditional model-based approaches. This survey provides a detailed analysis of the composition, capabilities, strengths, and limitations of each framework type. Furthermore, we identify critical research gaps and propose future directions aimed at achieving a more integrated and efficient framework for bipedal locomotion, with potential broad applications in everyday life.

翻译：双足机器人因其潜在应用前景及人工智能领域的技术进步，特别是深度强化学习（DRL）的突破，正日益受到全球关注。尽管深度强化学习已显著推动双足步态控制的发展，但构建能够灵活执行多样化任务的通用统一框架仍是当前挑战。本综述系统性地对现有双足步态控制的深度强化学习框架进行分类、比较与总结，将其归纳为端到端框架与分层控制框架两类。端到端框架基于学习方法进行评估，而分层框架则按层解析，各层分别采用基于学习的方法或传统基于模型的方法。本综述深入剖析每类框架的组成结构、能力优势与局限性，进而识别关键研究空白，并提出面向实现更集成化、高效化的双足步态控制框架的未来研究方向，该框架有望在日常生活场景中获得广泛应用。