不再有盲区：学习基于视觉的全方位双足运动以应对具有挑战性的地形
No More Blind Spots: Learning Vision-Based Omnidirectional Bipedal Locomotion for Challenging Terrain
AI总结：
本文提出一种基于视觉的全方位双足运动学习框架，通过减少渲染成本提升在复杂地形中的运动能力，首次展示其在多样化地形中的适应性。
摘要：
在动态环境中有效实现双足运动，如杂乱的室内空间或不平地形，需要在所有方向上具有敏捷和适应性。这需要全方位地形感知和能够处理此类输入的控制器。我们提出了一种基于视觉的全方位双足运动学习框架，利用深度图像实现无缝运动。关键挑战是模拟中渲染全方位深度图像的高计算成本，使传统仿真到现实强化学习（RL）不切实际。我们的方法结合了稳健的盲控制器和一个教师策略，监督基于视觉的学生策略，通过噪声增强的地形数据进行训练，以避免在RL中渲染成本并确保鲁棒性。我们还引入了一种数据增强技术用于监督学生训练，比传统方法快10倍。我们的框架通过仿真和现实测试验证，展示了在低成本渲染下有效的全方位运动能力。据我们所知，这是首次展示基于视觉的全方位双足运动，展示了其在多样化地形中的适应性。
英文摘要：
Effective bipedal locomotion in dynamic environments, such as cluttered indoor spaces or uneven terrain, requires agile and adaptive movement in all directions. This necessitates omnidirectional terrain sensing and a controller capable of processing such input. We present a learning framework for vision-based omnidirectional bipedal locomotion, enabling seamless movement using depth images. A key challenge is the high computational cost of rendering omnidirectional depth images in simulation, making traditional sim-to-real reinforcement learning (RL) impractical. Our method combines a robust blind controller with a teacher policy that supervises a vision-based student policy, trained on noise-augmented terrain data to avoid rendering costs during RL and ensure robustness. We also introduce a data augmentation technique for supervised student training, accelerating training by up to 10 times compared to conventional methods. Our framework is validated through simulation and real-world tests, demonstrating effective omnidirectional locomotion with minimal reliance on expensive rendering. This is, to the best of our knowledge, the first demonstration of vision-based omnidirectional bipedal locomotion, showcasing its adaptability to diverse 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。
