Performing agile navigation with four-legged robots is a challenging task because of the highly dynamic motions, contacts with various parts of the robot, and the limited field of view of the perception sensors. Here, we propose a fully learned approach to training such robots and conquer scenarios that are reminiscent of parkour challenges. The method involves training advanced locomotion skills for several types of obstacles, such as walking, jumping, climbing, and crouching, and then using a high-level policy to select and control those skills across the terrain. Thanks to our hierarchical formulation, the navigation policy is aware of the capabilities of each skill, and it will adapt its behavior depending on the scenario at hand. In addition, a perception module was trained to reconstruct obstacles from highly occluded and noisy sensory data and endows the pipeline with scene understanding. Compared with previous attempts, our method can plan a path for challenging scenarios without expert demonstration, offline computation, a priori knowledge of the environment, or taking contacts explicitly into account. Although these modules were trained from simulated data only, our real-world experiments demonstrate successful transfer on hardware, where the robot navigated and crossed consecutive challenging obstacles with speeds of up to 2 meters per second.

中文翻译：

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ANYmal 跑酷：学习四足机器人的敏捷导航

由于高度动态的运动、与机器人各个部分的接触以及感知传感器的有限视野，四足机器人执行敏捷导航是一项具有挑战性的任务。在这里，我们提出了一种完全学习的方法来训练此类机器人并克服让人想起跑酷挑战的场景。该方法涉及训练针对多种类型障碍的高级运动技能，例如行走、跳跃、攀爬和蹲伏，然后使用高级策略在整个地形上选择和控制这些技能。由于我们的分层制定，导航策略了解每种技能的功能，并且它将根据当前的场景调整其行为。此外，感知模块经过训练，可以从高度遮挡和嘈杂的感知数据中重建障碍物，并赋予管道场景理解能力。与之前的尝试相比，我们的方法可以为具有挑战性的场景规划一条路径，无需专家演示、离线计算、环境先验知识或明确考虑接触。尽管这些模块仅根据模拟数据进行训练，但我们的现实世界实验证明了硬件上的成功传输，其中机器人以高达每秒 2 米的速度导航并跨越连续的挑战性障碍。