Walking with Terrain Reconstruction: Learning to Traverse Risky Sparse Footholds
Ruiqi Yu, Qianshi Wang, Yizhen Wang, Zhicheng Wang, Jun Wu, Qiuguo Zhu

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…
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Taxonomy
TopicsAgriculture, Land Use, Rural Development · American Environmental and Regional History
