# Traversing Narrow Paths: A Two-Stage Reinforcement Learning Framework for Robust and Safe Humanoid Walking

## TL;DR

This paper introduces a two-stage reinforcement learning framework combining template-based planning and perception-aided modification to enable humanoid robots to safely and accurately traverse narrow paths, improving success rates and robustness.

## Contribution

The paper presents a novel two-stage training framework that integrates physics-based planning with reinforcement learning for improved narrow path traversal.

## Key findings

- Outperforms baseline methods in success rate and safety margins
- Successfully traverses a 0.2m wide beam in 20 trials without failure
- Enables effective sim-to-real transfer for humanoid navigation

## Abstract

Traversing narrow paths is challenging for humanoid robots due to the sparse and safety-critical footholds required. Purely template-based or end-to-end reinforcement learning-based methods suffer from such harsh terrains. This paper proposes a two stage training framework for such narrow path traversing tasks, coupling a template-based foothold planner with a low-level foothold tracker from Stage-I training and a lightweight perception aided foothold modifier from Stage-II training. With the curriculum setup from flat ground to narrow paths across stages, the resulted controller in turn learns to robustly track and safely modify foothold targets to ensure precise foot placement over narrow paths. This framework preserves the interpretability from the physics-based template and takes advantage of the generalization capability from reinforcement learning, resulting in easy sim-to-real transfer. The learned policies outperform purely template-based or reinforcement learning-based baselines in terms of success rate, centerline adherence and safety margins. Validation on a Unitree G1 humanoid robot yields successful traversal of a 0.2m wide and 3m long beam for 20 trials without any failure.