DreamWaQ++: 基于障碍物感知的四足机器人运动控制与鲁棒多模强化学习
DreamWaQ++: Obstacle-Aware Quadrupedal Locomotion With Resilient Multi-Modal Reinforcement Learning
AI总结：
DreamWaQ++通过鲁棒多模强化学习融合本体和外源感觉，实现四足机器人在复杂环境中的鲁棒运动控制与敏捷性能。
摘要：
四足机器人在导航复杂环境方面具有广阔的应用前景，其运动控制的鲁棒性堪比动物同类。然而，其浮动基配置使其容易受到现实世界不确定性的干扰，导致运动控制面临重大挑战。深度强化学习已成为实现稳健运动控制器的一种可行替代方案。然而，仅依赖本体感觉的方法会牺牲无碰撞运动，因为它们需要前足接触以检测台阶的存在以适应运动步态。同时，引入外源感觉需要长时间精确建模的由外源传感器观察的地图。因此，本文提出了一种新的方法，融合本体感觉和外源感觉，特征是鲁棒的多模强化学习。所提出的方法产生了一个控制器，使四足机器人在各种现实世界课程上表现出敏捷的运动性能，包括粗糙地形、陡坡和高楼台阶，同时保持对分布外情况的鲁棒性。
英文摘要：
Quadrupedal robots hold promising potential for applications in navigating cluttered environments with resilience akin to their animal counterparts. However, their floating base configuration makes them vulnerable to real-world uncertainties, yielding substantial challenges in their locomotion control. Deep reinforcement learning has become one of the plausible alternatives for realizing a robust locomotion controller. However, the approaches that rely solely on proprioception sacrifice collision-free locomotion because they require front-feet contact to detect the presence of stairs to adapt the locomotion gait. Meanwhile, incorporating exteroception necessitates a precisely modeled map observed by exteroceptive sensors over a period of time. Therefore, this work proposes a novel method to fuse proprioception and exteroception featuring a resilient multi-modal reinforcement learning. The proposed method yields a controller that showcases agile locomotion performance on a quadrupedal robot over a myriad of real-world courses, including rough terrains, steep slopes, and high-rise stairs, while retaining its robustness against out-of-distribution situations.
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。
