Perceptive Autonomous Stair Climbing for Quadrupedal Robots

This paper studies autonomous stair climbing for quadrupedal robots with perception. Enabling quadrupeds to reliably climb staircases greatly expands their applicability in practical scenarios. For this structured task, we develop a simple yet effective perception and control framework for autonomous quadrupedal stair climbing. By exploiting the structural knowledge about the staircases, the proposed framework first extracts the geometric information about the staircase from measurements of the perception system. Then, the climbing velocity and associated foothold references during stair climbing are generated via simple optimization algorithms based on the geometric information about the staircase. Given these references, we use model predictive control based approach to generate input joint torques for controlling the quadruped to complete the whole stair climbing task. Simulation validations using the full dynamic model of the Unitree's Aliengo quadruped with the MuJoCo simulator are performed, which demonstrate successful autonomous climbing of various staircases with different geometries. Effectiveness of the proposed strategy is further validated through hardware experiments on the real Aliengo robot with different real-world staircases.