通过显式足位图与动态稳定奖励，提升四足机器人在复杂地形上的精准稳定行走能力。
Learning Locomotion on Complex Terrain for Quadrupedal Robots with Foot Position Maps and Stability Rewards
- 引入足位图与注意力框架结合，显式控制足部位置。
- 在域内和域外地形上均实现更高行走成功率。
- 适合需要高精度与鲁棒性的复杂地形机器人应用。
四足机器人在复杂地形上的运动是机器人学长期研究课题。尽管基于强化学习的运动方法提升了泛化能力和足部定位精度，但其依赖关节角隐式推断足位，缺乏优化方法的显式精度与稳定性保障。为此，本文将足位图融入高度图，并在注意力框架中引入动态运动-稳定性奖励，实现复杂地形上的高效运动。我们在训练时见过的地形以及域外（OOD）地形上进行了广泛验证。结果表明，该方法可实现精确且稳定的移动，在域内与域外地形上均显著提升行走成功概率。
原文摘要 · Abstract (English)
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.
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