现在你看到了那一切：从原始像素学习端到端的人形 locomotion
Now You See That: Learning End-to-End Humanoid Locomotion from Raw Pixels
AI总结：
本文提出端到端人形运动框架，通过高保真深度传感器模拟和视觉感知行为蒸馏，解决仿真到现实转移和地形适应问题，展示在不同人形平台上稳健性能。
摘要：
实现稳健的基于视觉的人形运动仍具有挑战性，由于两个根本问题：仿真到现实的差距引入了显著的感知噪声，影响精细任务性能；训练统一策略跨多样地形受冲突学习目标阻碍。为此，我们提出端到端的视觉驱动人形运动框架。为实现稳健的仿真到现实转移，我们开发了高保真度深度传感器模拟，捕捉立体匹配伪影和校准不确定性。我们进一步提出视觉感知行为蒸馏方法，结合潜在空间对齐与噪声不变辅助任务，使知识从特权高度图有效转移到噪声深度观测。为实现多样地形适应，我们引入地形特定奖励塑造，结合多批评者和多判别器学习，其中专用网络捕捉每种地形类型的动态和运动先验。我们验证了该方法在两个配备不同立体深度相机的人形平台上。所得到的策略在多样化环境中表现出稳健性能，无缝处理极端挑战如高平台和宽间隙，以及精细任务如双向长距离台阶穿越。
英文摘要：
Achieving robust vision-based humanoid locomotion remains challenging due to two fundamental issues: the sim-to-real gap introduces significant perception noise that degrades performance on fine-grained tasks, and training a unified policy across diverse terrains is hindered by conflicting learning objectives. To address these challenges, we present an end-to-end framework for vision-driven humanoid locomotion. For robust sim-to-real transfer, we develop a high-fidelity depth sensor simulation that captures stereo matching artifacts and calibration uncertainties inherent in real-world sensing. We further propose a vision-aware behavior distillation approach that combines latent space alignment with noise-invariant auxiliary tasks, enabling effective knowledge transfer from privileged height maps to noisy depth observations. For versatile terrain adaptation, we introduce terrain-specific reward shaping integrated with multi-critic and multi-discriminator learning, where dedicated networks capture the distinct dynamics and motion priors of each terrain type. We validate our approach on two humanoid platforms equipped with different stereo depth cameras. The resulting policy demonstrates robust performance across diverse environments, seamlessly handling extreme challenges such as high platforms and wide gaps, as well as fine-grained tasks including bidirectional long-term staircase traversal.
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。
