SURF-Loco: Mastering Complex Industrial Terrains with 3D Surfel-Based Reinforcement Learning for Legged Robots

Abstract

Legged robots offer significant potential for navigating complex industrial terrains, but their capabilities are often constrained by perception systems struggling to interpret intricate 3D geometry. Conventional 2D/2.5D representations like depth or elevation maps fail to capture complex 3D geometry, leading to unsafe locomotion. This paper presents SURF-Loco, a novel framework that enables robust legged locomotion by learning directly from a 3D surfel-based model. Our approach uses surfels to create an omnidirectional representation that explicitly encodes the geometric properties necessary for stable locomotion. We integrate this structured 3D representation into an end-to-end Mixture-of-Experts (MoE) reinforcement learning policy. A variational autoencoder (VAE) distills the complex 3D surroundings into a compact latent context. This geometric context enables a gating network to dynamically select expert sub-policies for agile, context-aware actions. We validate our method on hexapod robots, achieving robust zero-shot sim-to-real transfer on a variety of challenging industrial obstacles.