歩行ポリシーに基づく3次元ボクセル表現を用いた走破性学習
Locomotion Policy Guided Traversability Learning using Volumetric Representations of Complex Environments
物理シミュレータで大量の歩行経験を収集し、3Dボクセル占有マップから歩行ポリシーに適した走破性コストを予測する疎畳み込みネットワークを学習、脚式ロボットの経路計画に応用した。
著者: Jonas Frey, David Hoeller, Shehryar Khattak, Marco Hutter
分類: cs.RO
原文アブストラクト
Despite the progress in legged robotic locomotion, autonomous navigation in unknown environments remains an open problem. Ideally, the navigation system utilizes the full potential of the robots' locomotion capabilities while operating within safety limits under uncertainty. The robot must sense and analyze the traversability of the surrounding terrain, which depends on the hardware, locomotion control, and terrain properties. It may contain information about the risk, energy, or time consumption needed to traverse the terrain. To avoid hand-crafted traversability cost functions we propose to collect traversability information about the robot and locomotion policy by simulating the traversal over randomly generated terrains using a physics simulator. Thousand of robots are simulated in parallel controlled by the same locomotion policy used in reality to acquire 57 years of real-world locomotion experience equivalent. For deployment on the real robot, a sparse convolutional network is trained to predict the simulated traversability cost, which is tailored to the deployed locomotion policy, from an entirely geometric representation of the environment in the form of a 3D voxel-occupancy map. This representation avoids the need for commonly used elevation maps, which are error-prone in the presence of overhanging obstacles and multi-floor or low-ceiling scenarios. The effectiveness of the proposed traversability prediction network is demonstrated for path planning for the legged robot ANYmal in various indoor and natural environments.
