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 demonstration can be found at https://youtu.be/OTzlWzDfAe8, and the codebase at https://github.com/jennyzzt/perceptual-locomotion.

翻译：本研究开发了一种面向感知型腿足运动的深度学习框架，该框架融合了视觉反馈、本体感知信息及足地接触的主动步态调节功能。感知模块仅需前置单目相机获取地形高度图，而步态周期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策略实现。借助该框架，用户可根据不同地形遍历需求，下达任务级指令以控制行走速度与步态频率，显著提升对障碍物的可靠应对能力。实验表明，所习得的感知运动策略能精准遵循任务级控制指令产生预期行为，并在未知地形与外部力扰动下保持鲁棒性。视频演示见https://youtu.be/OTzlWzDfAe8，代码库见https://github.com/jennyzzt/perceptual-locomotion。