RPL：在复杂地形上学习鲁棒的人形感知移动
RPL: Learning Robust Humanoid Perceptive Locomotion on Challenging Terrains
AI总结：
RPL通过两阶段训练框架在复杂地形上实现稳健的多方向人形移动，结合深度蒸馏和增强技术，提升机器人负载下的移动能力。
摘要：
人形感知移动已取得显著进展并展现出巨大潜力，但实现稳健的多方向移动仍处于探索阶段。为解决这一挑战，我们提出了RPL，一种两阶段训练框架，能够使机器人在复杂地形上实现多方向移动，并且在负载下保持稳健。RPL首先使用特权高度图观察训练地形特定的专家策略，以掌握不同地形上的解耦移动和操作技能，然后将这些策略蒸馏成一个transformer策略，利用多个深度相机覆盖广泛视角。在蒸馏过程中，我们引入了两种技术来增强多方向移动的稳健性：基于速度命令的深度特征缩放和随机侧遮挡，这对于不对称的深度观察和未见的地形宽度至关重要。为了实现可扩展的深度蒸馏，我们开发了一个高效的多深度系统，该系统能够对动态机器人网格和静态地形网格进行射线投射，从而在大规模并行环境中实现比现有模拟器深度渲染管道快5倍的速度，同时建模真实的传感器延迟、噪声和丢失。广泛的现实世界实验展示了在挑战性地形上实现负载（2kg）的稳健多方向移动，包括20°坡度、不同步长（22 cm、25 cm、30 cm）的台阶，以及25 cm x 25 cm的台阶之间相隔60 cm的空隙。
英文摘要：
Humanoid perceptive locomotion has made significant progress and shows great promise, yet achieving robust multi-directional locomotion on complex terrains remains underexplored. To tackle this challenge, we propose RPL, a two-stage training framework that enables multi-directional locomotion on challenging terrains, and remains robust with payloads. RPL first trains terrain-specific expert policies with privileged height map observations to master decoupled locomotion and manipulation skills across different terrains, and then distills them into a transformer policy that leverages multiple depth cameras to cover a wide range of views. During distillation, we introduce two techniques to robustify multi-directional locomotion, depth feature scaling based on velocity commands and random side masking, which are critical for asymmetric depth observations and unseen widths of terrains. For scalable depth distillation, we develop an efficient multi-depth system that ray-casts against both dynamic robot meshes and static terrain meshes in massively parallel environments, achieving a 5-times speedup over the depth rendering pipelines in existing simulators while modeling realistic sensor latency, noise, and dropout. Extensive real-world experiments demonstrate robust multi-directional locomotion with payloads (2kg) across challenging terrains, including 20° slopes, staircases with different step lengths (22 cm, 25 cm, 30 cm), and 25 cm by 25 cm stepping stones separated by 60 cm gaps.
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。
