Abstract
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.
Abstract (translated)
双向行走机器人因其在人工智能的应用和进步而引起了全球关注。特别是深度强化学习（DRL），它们在行走方面的进展尤为明显。虽然DRL在行走方面取得了显著的进展，但开发一个全面统一框架，能巧妙地执行各种任务仍然具有挑战性。本调查系统地分类、比较并总结了现有的DRL框架，将它们组织成端到端的控制方案。基于学习方法的端到端框架是根据它们的学习方式进行评估的，而基于传统模型的层次框架则是通过利用学习方法或传统模型基方法来分割的。本调查详细分析了每种框架的构成、功能、优缺点和局限性。此外，我们识别出关键的研究空白并提出了旨在实现更集成和高效的下肢行走框架的未来方向，该框架在日常生活活动中具有广泛的应用潜力。
URL
https://arxiv.org/abs/2404.17070
