This work developed a learning framework for perceptive legged locomotion that combines visual feedback, proprioceptive information, and active gait regulation of foot-ground contacts. The perception requires only one forward-facing camera to obtain the heightmap, and the active regulation of gait paces and traveling velocity are realized through our formulation of CPG-based high-level imitation of foot-ground contacts. Through this framework, an end-user has the ability to command task-level inputs to control different walking speeds and gait frequencies according to the traversal of different terrains, which enables more reliable negotiation with encountered obstacles. The results demonstrated that the learned perceptive locomotion policy followed task-level control inputs with intended behaviors and was robust in presence of unseen terrains and external force perturbations. A video of the project can be found at https://youtu.be/OTzlWzDfAe8.

翻译：本文开发了一种用于感知四足运动的深度学习框架，该框架融合了视觉反馈、本体感知信息以及足地接触的主动步态调节机制。感知仅需一个前置摄像头获取地形高程图，步频与行进速度的主动调节则通过基于CPG的足地接触高级模仿方法实现。借助该框架，终端用户可依据不同地形遍历需求，通过任务级输入指令控制行走速度与步态频率，从而更可靠地应对行进中的障碍物。实验结果表明，该学习感知运动策略能按预设行为响应任务级控制指令，且在未知地形与外力扰动下仍具有鲁棒性。项目演示视频见https://youtu.be/OTzlWzDfAe8。