Walking with Terrain Reconstruction: Learning to Traverse Risky Sparse Footholds

TL;DR

This paper presents a reinforcement learning approach for legged robots to traverse risky terrains using only proprioception and depth images, enhanced by local terrain reconstruction for better decision-making.

Contribution

It introduces a novel local terrain reconstruction method that improves terrain understanding for reinforcement learning-based locomotion without relying on complex mapping pipelines.

Findings

Successful real-world deployment on a quadrupedal robot

Effective traversal of highly sparse and risky terrains

Outperforms existing methods in agility and adaptability

Abstract

Traversing risky terrains with sparse footholds presents significant challenges for legged robots, requiring precise foot placement in safe areas. To acquire comprehensive exteroceptive information, prior studies have employed motion capture systems or mapping techniques to generate heightmap for locomotion policy. However, these approaches require specialized pipelines and often introduce additional noise. While depth images from egocentric vision systems are cost-effective, their limited field of view and sparse information hinder the integration of terrain structure details into implicit features, which are essential for generating precise actions. In this paper, we demonstrate that end-to-end reinforcement learning relying solely on proprioception and depth images is capable of traversing risky terrains with high sparsity and randomness. Our method introduces local terrain reconstruction, leveraging the benefits of clear features and sufficient information from the heightmap, which serves as an intermediary for visual feature extraction and motion generation. This allows the policy to effectively represent and memorize critical terrain information. We deploy the proposed framework on a low-cost quadrupedal robot, achieving agile and adaptive locomotion across various challenging terrains and showcasing outstanding performance in real-world scenarios. Video at: youtu.be/Rj9v5EZsn-M.