改进的无膝足机器人实现更快行走，结合动态规划与强化学习。
Extended Hybrid Zero Dynamics for Bipedal Walking of the Knee-less Robot SLIDER
- 提出扩展混合零动力学方法，适配无膝腿机器人
- 实测行走速度提升150%，超越传统模型预测控制
- 适合研究高效足式机器人控制与实时策略生成
无膝足机器人如SLIDER具有超轻腿结构和更高的行走能效优势。本文首先改进了SLIDER的硬件设计，采用新型线性足部与更优质量分布，实现更高运动速度；其次提出一种适用于滑动关节机器人的扩展混合零动力学（eHZD）方法，可离线生成不同参考速度的步态库；第三，提出一种引导式深度强化学习（Guided DRL）算法，利用预生成步态库实时构建行走控制策略。该方法结合了HZD的全动力学稳定性与DRL的实时自适应能力。实验表明，该方法使行走速度比先前基于MPC的方法提高150%。
原文摘要 · Abstract (English)
Knee-less bipedal robots like SLIDER have the advantage of ultra-lightweight legs and improved walking energy efficiency compared to traditional humanoid robots. In this paper, we firstly introduce an improved hardware design of the SLIDER bipedal robot with new line-feet and more optimized mass distribution that enables higher locomotion speeds. Secondly, we propose an extended Hybrid Zero Dynamics (eHZD) method, which can be applied to prismatic joint robots like SLIDER. The eHZD method is then used to generate a library of gaits with varying reference velocities in an offline way. Thirdly, a Guided Deep Reinforcement Learning (DRL) algorithm is proposed to use the pre-generated library to create walking control policies in real-time. This approach allows us to combine the advantages of both HZD (for generating stable gaits with a full-dynamics model) and DRL (for real-time adaptive gait generation). The experimental results show that this approach achieves 150% higher walking velocity than the previous MPC-based approach.
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。