3D表現を用いた狭所歩行の学習
Learning to walk in confined spaces using 3D representation
強化学習と3Dボリューム表現を組み合わせ、二層の階層型ポリシーで四足ロボットが狭く起伏のある環境を歩行できるようにした研究。
著者: Takahiro Miki, Joonho Lee, Lorenz Wellhausen, Marco Hutter
分類: cs.RO
原文アブストラクト
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.
