超越自身视角限制：基于多视角深度的学习用于稳健的四足运动
Beyond Egocentric Limits: Multi-View Depth-Based Learning for Robust Quadrupedal Locomotion
AI总结：
本文提出基于多视角深度学习的四足运动框架，通过结合自身和外视角信息提升运动的鲁棒性和敏捷性。
摘要：
最近在腿部运动领域的进展使机器人能够实现高度动态和类似公园our的行为，类似于其生物对应物。然而，这些方法大多依赖于自身视角（第一人称）的感知，限制了其性能，尤其是在机器人视角被遮挡时。一个有前景的解决方案是通过使用互补的视角，如多个主体交换感知信息来增强机器人的环境意识。受这一想法的启发，本文提出了一种基于多视角深度的运动框架，结合自身视角和外视角的观察，以在敏捷运动中提供更丰富的环境上下文。使用教师-学生知识蒸馏方法，学生策略学习将本体感觉与双深度流融合，同时保持对现实世界传感不完美的鲁棒性。为了进一步提高鲁棒性，我们引入了广泛的领域随机化，包括随机远程摄像头丢失和3D位置扰动，以模拟空-地协同感知。模拟结果表明，多视角策略在跨越缝隙、下台阶和其他动态动作中优于单视角基线，同时在外视角摄像头部分或完全不可用时保持稳定。此外，实验表明，当训练期间引入适度的视角偏移时，能够很好地容忍。本研究证明了异质视觉反馈提高了四足运动的鲁棒性和敏捷性。此外，为了支持可重复性，本文的实现已公开在https://anonymous.4open.science/r/multiview-parkour-6FB8。
英文摘要：
Recent progress in legged locomotion has allowed highly dynamic and parkour-like behaviors for robots, similar to their biological counterparts. Yet, these methods mostly rely on egocentric (first-person) perception, limiting their performance, especially when the viewpoint of the robot is occluded. A promising solution would be to enhance the robot's environmental awareness by using complementary viewpoints, such as multiple actors exchanging perceptual information. Inspired by this idea, this work proposes a multi-view depth-based locomotion framework that combines egocentric and exocentric observations to provide richer environmental context during agile locomotion. Using a teacher-student distillation approach, the student policy learns to fuse proprioception with dual depth streams while remaining robust to real-world sensing imperfections. To further improve robustness, we introduce extensive domain randomization, including stochastic remote-camera dropouts and 3D positional perturbations that emulate aerial-ground cooperative sensing. Simulation results show that multi-viewpoints policies outperform single-viewpoint baseline in gap crossing, step descent, and other dynamic maneuvers, while maintaining stability when the exocentric camera is partially or completely unavailable. Additional experiments show that moderate viewpoint misalignment is well tolerated when incorporated during training. This study demonstrates that heterogeneous visual feedback improves robustness and agility in quadrupedal locomotion. Furthermore, to support reproducibility, the implementation accompanying this work is publicly available at https://anonymous.4open.science/r/multiview-parkour-6FB8
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。
