Legged robots are increasingly entering new domains and applications, including search and rescue, inspection, and logistics. However, for such systems to be valuable in real-world scenarios, they must be able to autonomously and robustly navigate irregular terrains. In many cases, robots that are sold on the market do not provide such abilities, being able to perform only blind locomotion. Furthermore, their controller cannot be easily modified by the end-user, requiring a new and time-consuming control synthesis. In this work, we present a fast local motion planning pipeline that extends the capabilities of a black-box walking controller that is only able to track high-level reference velocities. More precisely, we learn a set of motion models for such a controller that maps high-level velocity commands to Center of Mass (CoM) and footstep motions. We then integrate these models with a variant of the A star algorithm to plan the CoM trajectory, footstep sequences, and corresponding high-level velocity commands based on visual information, allowing the quadruped to safely traverse irregular terrains at demand.

翻译：四足机器人正日益进入新的领域和应用场景，包括搜索救援、巡检和物流。然而，要使此类系统在现实场景中具有实用价值，它们必须能够自主且稳健地穿越不规则地形。在许多情况下，市售机器人并不具备此类能力，仅能执行盲目的运动。此外，终端用户难以轻松修改其控制器，因而需要耗时进行全新的控制综合。在本工作中，我们提出了一种快速局部运动规划流程，用于扩展仅能跟踪高层速度指令的黑箱行走控制器的能力。具体而言，我们为此类控制器学习了一组运动模型，该模型能将高层速度指令映射为质心和步态运动。随后，我们将这些模型与A*算法的一种变体相集成，基于视觉信息规划质心轨迹、步态序列及对应的高层速度指令，使四足机器人能够按需安全穿越不规则地形。