基于足部位置图和稳定性奖励的四足机器人复杂地形运动学习
Learning Locomotion on Complex Terrain for Quadrupedal Robots with Foot Position Maps and Stability Rewards
AI总结：
本文提出结合高度图的足部位置图和基于注意力的动态运动-稳定性奖励，提升四足机器人在复杂地形中的运动精度与稳定性。
摘要：
四足机器人在复杂地形中的运动一直是机器人学中的长期研究课题。尽管最近基于强化学习的运动方法提高了通用性和足部定位精度，但它们依赖于从关节角度隐含推断足部位置，缺乏基于优化方法的显式精度和稳定性保障。为此，我们引入集成到高度图中的足部位置图，并在基于注意力的框架中引入动态运动-稳定性奖励，以实现复杂地形的运动。我们在训练期间见过的地形以及域外（OOD）地形上广泛验证了我们的方法。结果表明，所提方法能够实现精确且稳定的运动，在域内和域外地形上均提高了运动成功率。
英文摘要：
Quadrupedal locomotion over complex terrain has been a long-standing research topic in robotics. While recent reinforcement learning-based locomotion methods improve generalizability and foot-placement precision, they rely on implicit inference of foot positions from joint angles, lacking the explicit precision and stability guarantees of optimization-based approaches. To address this, we introduce a foot position map integrated into the heightmap, and a dynamic locomotion-stability reward within an attention-based framework to achieve locomotion on complex terrain. We validate our method extensively on terrains seen during training as well as out-of-domain (OOD) terrains. Our results demonstrate that the proposed method enables precise and stable movement, resulting in improved locomotion success rates on both in-domain and OOD 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。
