VAPOR：基于离线强化学习的户外植被环境中腿足机器人导航
VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement Learning
AI总结：
VAPOR利用离线强化学习训练策略，结合LiDAR代价地图与本体感觉数据学习植被障碍物属性，并通过上下文感知规划器自适应调整速度，在真实户外植被环境中显著提升了腿足机器人的导航成功率与效率。
摘要：
我们提出VAPOR，一种利用离线强化学习（RL）在非结构化、植被茂密的户外环境中实现自主腿足机器人导航的新方法。我们的方法使用演员-评论家网络和真实户外植被中采集的任意数据来训练一种新的RL策略。该策略以基于高度和强度的代价地图（由3D LiDAR点云生成）、目标代价地图以及处理后的本体感觉数据作为状态输入，并学习周围障碍物的物理和几何属性，如高度、密度和坚实度/刚度。随后，使用完全训练后的策略的评论家网络来评估由一种新颖的上下文感知规划器生成的动态可行速度的质量。该规划器根据是否存在导致被困的植被以及密集环境中的狭窄通道来自适应调整机器人的速度空间。我们在复杂的真实户外场景（包括茂密植被）中，在Spot机器人上展示了该方法的能力。我们观察到，与现有的端到端离线RL和其他户外导航方法相比，VAPOR的动作将成功率提高了最多40%，平均电流消耗降低了最多2.9%，归一化轨迹长度缩短了最多11.2%。
英文摘要：
We present VAPOR, a novel method for autonomous legged robot navigation in unstructured, densely vegetated outdoor environments using offline Reinforcement Learning (RL). Our method trains a novel RL policy using an actor-critic network and arbitrary data collected in real outdoor vegetation. Our policy uses height and intensity-based cost maps derived from 3D LiDAR point clouds, a goal cost map, and processed proprioception data as state inputs, and learns the physical and geometric properties of the surrounding obstacles such as height, density, and solidity/stiffness. The fully-trained policy's critic network is then used to evaluate the quality of dynamically feasible velocities generated from a novel context-aware planner. Our planner adapts the robot's velocity space based on the presence of entrapment inducing vegetation, and narrow passages in dense environments. We demonstrate our method's capabilities on a Spot robot in complex real-world outdoor scenes, including dense vegetation. We observe that VAPOR's actions improve success rates by up to 40%, decrease the average current consumption by up to 2.9%, and decrease the normalized trajectory length by up to 11.2% compared to existing end-to-end offline RL and other outdoor navigation methods.
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。
