High-speed control and navigation for quadrupedal robots on complex and discrete terrain

High-speed legged navigation in discrete and geometrically complex environments is a highly challenging task due to the high-degree-of-freedom dynamics and long-horizon, non-convex nature of the optimization problem. In this work, we propose a hierarchical navigation pipeline for legged robots that is capable of traversing such environments at high speed. The proposed pipeline consists of a planner and tracker module. The planner module finds physically feasible foothold plans by sampling-based optimization strategy, which involves sequential filtering. This filtering process utilizes multiple criteria, including simple heuristics and a learned neural network, to quickly eliminate bad samples. Subsequently, rollouts are performed in a physics simulation to identify the best foothold plan concerning the engineered cost function and to confirm their physical consistency. This hierarchical planning module is computationally efficient and physically accurate at the same time. The tracker aims to accurately step on the target footholds from the planning module. During the training stage, the foothold target distribution is given by a generative model which is trained adversarially with the tracker. This process ensures that the tracker is trained in a sufficiently difficult environment. The resulting tracker is capable of overcoming terrains that are more difficult than what the previous methods could manage. We demonstrate this using Raibo, our in-house dynamic quadrupedal robot. The results are highly dynamic and agile motions: Raibo is capable of running on vertical walls, jumping a 1.3m gap, running over stepping stones at 4 m/s, and autonomously navigating on terrains full of 30-degree ramps, stairs, and boxes of various sizes.