Knowledge-Based Locomotion Policy for Quadruped Robots under Incomplete Terrain Observation

Abstract

Body-mounted LiDAR sensors suffer from systematic blind spots during stair locomotion, creating a partial observability problem that single-step terrain snapshots cannot resolve. We address this with a recurrent locomotion policy for the Unitree Go2 that builds implicit knowledge of stair geometry through a GRU-based recurrent encoder over pointcloud and proprioceptive inputs, enabling robust stair ascent and descent even under occluded LiDAR conditions. Ablation experiments show that masking pointcloud input at inference time causes catastrophic failure on stair terrain and severe performance degradation overall, confirming that implicit stair knowledge is a critical cue for step negotiation rather than a merely complementary signal.