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.

翻译：然而,在模拟中很难产生多样化的、具有挑战性的和现实的、不结构化的地形,这限制了研究人员评估其移动政策的方式。 在本文中,我们通过地形写作和积极学习,对地形数据集的生成进行原型,而有学识的采样员可以悄悄地生成各种高质量的地形。我们期望生成的数据集能够形成一个地形-腐蚀性基准,用以测量腿动。数据集、代码执行和一些政策评价将在https://bit.ly/3bn4j7f上发布。