Quadrupedal Footstep Planning using Learned Motion Models of a Black-Box Controller (IROS 23)

Quadrupedal Footstep Planning using Learned Motion Models of a Black-Box Controller. Ilyass Taouil, Giulio Turrisi, Daniel Schleich, Victor Barasuol, Claudio Semini, and Sven Behnke. IROS 2023. Abstract: Legged robots are increasingly entering new domains and applications, including search and rescue, inspection, and logistics. However, for such a 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* 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.