DPL: 実環境に即した深度合成とクロスアテンション地形再構成による深度のみの知覚型ヒューマノイド歩行
DPL: Depth-only Perceptive Humanoid Locomotion via Realistic Depth Synthesis and Cross-Attention Terrain Reconstruction
ノイズの多い深度画像から地形を再構成するクロスアテンショントランスフォーマと、自己遮蔽を考慮したリアルな深度画像合成手法を組み合わせ、深度センサのみで多様な地形を歩行できるヒューマノイドの学習フレームワークを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Jingkai Sun, Gang Han, Pihai Sun, Wen Zhao, Jiahang Cao, Jiaxu Wang, Qiang Zhang, Yijie Guo
分類: cs.RO
原文アブストラクト
Recent advancements in legged robot perceptive locomotion have shown promising progress. However, terrain-aware humanoid locomotion remains largely constrained to two paradigms: depth image-based end-to-end learning and elevation map-based methods. The former suffers from limited training efficiency and a significant sim-to-real gap in depth perception, while the latter depends heavily on multiple vision sensors and localization systems, resulting in latency and reduced robustness. To overcome these challenges, we propose a novel framework that tightly integrates three key components: (1) Terrain-Aware Locomotion Policy with a Blind Backbone, which leverages pre-trained elevation map-based perception to guide reinforcement learning with minimal visual input; (2) Multi-Modality Cross-Attention Transformer, which reconstructs structured terrain representations from noisy depth images; (3) Realistic Depth Images Synthetic Method, which employs self-occlusion-aware ray casting and noise-aware modeling to synthesize realistic depth observations, achieving over 30\% reduction in terrain reconstruction error. This combination enables efficient policy training with limited data and hardware resources, while preserving critical terrain features essential for generalization. We validate our framework on a full-sized humanoid robot, demonstrating agile and adaptive locomotion across diverse and challenging terrains.
