足元に注意：雑然とした環境での意味論ガイド付き歩行学習
Watch Your Step: Learning Semantically-Guided Locomotion in Cluttered Environment
雑然とした環境で低い障害物を避けるため、意味情報と幾何情報を統合した二段階強化学習フレームワークSemLocoを提案し、衝突を大幅に減らすことを実証した。
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著者: Denan Liang, Yuan Zhu, Ruimeng Liu, Thien-Minh Nguyen, Shenghai Yuan, Lihua Xie
分類: cs.RO
原文アブストラクト
Although legged robots demonstrate impressive mobility on rough terrain, using them safely in cluttered environments remains a challenge. A key issue is their inability to avoid stepping on low-lying objects, such as high-cost small devices or cables on flat ground. This limitation arises from a disconnection between high-level semantic understanding and low-level control, combined with errors in elevation maps during real-world operation. To address this, we introduce SemLoco, a Reinforcement Learning (RL) framework designed to avoid obstacles precisely in densely cluttered environments. SemLoco uses a two-stage RL approach that combines both soft and hard constraints. It performs pixel-wise foothold safety inference, which enables more accurate foot placement. Additionally, SemLoco integrates semantic map, allowing it to assign traversability costs instead of relying only on geometric data. SemLoco greatly reduces collisions and improves safety around sensitive objects, enabling reliable navigation in situations where traditional controllers would likely cause damage. Experimental results further show that SemLoco can be effectively applied to more complex, unstructured real-world environments. A demo video can be view at https://youtu.be/FSq-RSmIxOM.
