论文标题
为腿部运动生成地形稳定基准：通过地形和积极学习的原型
Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning
论文作者
论文摘要
在腿部机器人技术中，地形感知的运动已成为一个新兴的话题。但是，很难在模拟中产生多种，具有挑战性和现实的非结构化地形，这限制了研究人员评估其运动政策的方式。在本文中，我们通过地形创作和积极的学习制作了地形数据集的产生，而学识渊博的采样器可以稳定地产生各种高质量的地形。我们希望生成的数据集为腿部运动做出了地形的基准测试。数据集，代码实现和某些策略评估在https://bit.ly/3bn4j7f上发布。
Terrain-aware locomotion has become an emerging topic in legged robotics. However, it is hard to generate diverse, challenging, and realistic unstructured terrains in simulation, which limits the way researchers evaluate their locomotion policies. In this paper, we prototype the generation of a terrain dataset via terrain authoring and active learning, and the learned samplers can stably generate diverse high-quality terrains. We expect the generated dataset to make a terrain-robustness benchmark for legged locomotion. The dataset, the code implementation, and some policy evaluations are released at https://bit.ly/3bn4j7f.
