利用3D表示学习在受限空间中行走
Learning to walk in confined spaces using 3D representation
AI总结：
提出结合强化学习与3D体素表示的两层分层运动控制方法，使四足机器人能在悬垂障碍和崎岖受限空间中鲁棒导航，并通过仿真与真实实验验证。
摘要：
腿足机器人凭借在行走过程中谨慎选择落脚点并灵活调整身体姿态的能力，有潜力穿越复杂地形并进入传统平台无法抵达的受限空间。然而，在真实世界应用中实现鲁棒部署仍是一项开放性挑战。本文提出一种利用强化学习和3D体素表示进行腿足运动控制的方法，以在受限且非结构化的环境中实现鲁棒、通用的运动。通过采用两层分层策略结构，我们利用高鲁棒性低层策略跟踪6D指令的能力，以及高层策略实现三维空间感知以在悬垂障碍物下方导航的能力。我们的研究包括开发程序化地形生成器，以创建多样化的训练环境。我们在仿真和真实世界两种设置中进行了一系列实验评估，证明了本方法在四足机器人于受限、崎岖地形中进行控制的有效性。通过实现这一目标，本工作将腿足机器人的适用性扩展到更广泛的场景。
英文摘要：
Legged robots have the potential to traverse complex terrain and access confined spaces beyond the reach of traditional platforms thanks to their ability to carefully select footholds and flexibly adapt their body posture while walking. However, robust deployment in real-world applications is still an open challenge. In this paper, we present a method for legged locomotion control using reinforcement learning and 3D volumetric representations to enable robust and versatile locomotion in confined and unstructured environments. By employing a two-layer hierarchical policy structure, we exploit the capabilities of a highly robust low-level policy to follow 6D commands and a high-level policy to enable three-dimensional spatial awareness for navigating under overhanging obstacles. Our study includes the development of a procedural terrain generator to create diverse training environments. We present a series of experimental evaluations in both simulation and real-world settings, demonstrating the effectiveness of our approach in controlling a quadruped robot in confined, rough terrain. By achieving this, our work extends the applicability of legged robots to a broader range of scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
