TAGA：面向可泛化敏捷人形运动的地形感知主动注视学习
TAGA: Terrain-aware Active Gaze Learning for Generalizable Agile Humanoid Locomotion
AI总结：
提出TAGA框架，通过融合视觉、本体感觉和运动命令，让模型学习主动注视地形关键区域，在有限计算资源下提高感知密度，实现鲁棒且可泛化的敏捷人形运动。
摘要：
在多样挑战性地形上的敏捷人形运动需要广泛的感知覆盖和精确的局部几何理解。受人类在运动中选择性注视相关地形的启发，我们提出了TAGA，一种用于基于注意力的人形控制的地形感知主动注视学习框架。通过融合视觉、本体感觉和运动命令，我们的框架引导模型学习预期线索并主动关注高度扫描的特定区域，选择性地将这些信息区域用于下游网络。这自适应地提高了在严格机载计算约束下观测的信息密度，从而在更大尺度地形上实现细粒度感知运动。我们发现，这种注视行为可以仅通过强化学习自然涌现，无需额外监督或显式指导，显著提高了训练效率。因此，训练后的策略在仿真和硬件上展示了鲁棒且可泛化的运动，包括可靠的地形感知落脚点选择、高台穿越、竞争性稀疏落脚点穿越，以及在感知人形运动系统中报告的最大实际间隙穿越距离1.2米，同时在严重感知干扰和环境干扰下保持稳定性。
英文摘要：
Agile humanoid locomotion across diverse challenging terrain demands both wide perceptual coverage and precise local geometry understanding. Motivated by the way humans selectively look at relevant terrain during locomotion, we introduce TAGA, a Terrain-aware Active Gaze learning framework for Attention-based humanoid control. By fusing vision, proprioception, and motion commands, our framework guides the model to learn anticipatory cues and actively attend to specific areas of the height scan, selectively using these informative regions for the downstream network. This adaptively increases the information density of observations under tight onboard computational constraints, thus enabling fine-grained perceptive locomotion over larger-scale terrains. We find that such gaze behaviors can naturally emerge through reinforcement learning alone, without requiring additional supervision or explicit guidance, significantly improving training efficiency. As a result, the trained policy demonstrates robust and generalizable locomotion in simulation and on hardware, including reliable terrain-aware foothold selection, elevated-platform traversal, competitive sparse-foothold traversal, and the largest reported real-world gap traversal distance of 1.2m among perceptive humanoid locomotion systems, while maintaining stability under severe perceptual disturbances and environmental interference.
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。
