DPL：基于真实深度合成与交叉注意力地形重建的仅深度感知人形机器人运动
DPL: Depth-only Perceptive Humanoid Locomotion via Realistic Depth Synthesis and Cross-Attention Terrain Reconstruction
AI总结：
该研究提出整合地形感知运动策略、多模态交叉注意力Transformer及真实深度图像合成方法的DPL框架，在人形机器人上实现高效训练与多样地形下的自适应运动。
摘要：
近年来，腿式机器人感知运动领域取得了令人鼓舞的进展，但感知地形的人形机器人运动仍主要局限于两种范式：基于深度图像的端到端学习和基于高程图的方法。前者存在训练效率有限、深度感知的现实差距显著的问题；后者则严重依赖多个视觉传感器和定位系统，导致延迟增加、鲁棒性降低。为克服这些挑战，我们提出了一种新框架，紧密整合三个关键组件：（1）带盲主干的地形感知运动策略，利用预训练的基于高程图的感知，以最少的视觉输入指导强化学习；（2）多模态交叉注意力Transformer，从噪声深度图像中重建结构化地形表示；（3）真实深度图像合成方法，采用自遮挡感知光线投射和噪声感知建模来合成真实的深度观测，使地形重建误差降低超过30%。这种组合能够在有限的数据和硬件资源下实现高效的策略训练，同时保留泛化所需的关键地形特征。我们在全尺寸人形机器人上验证了该框架，展示了其在多样且具有挑战性的地形上实现敏捷、自适应运动的能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
