Generating a Terrain-Robustness Benchmark for Legged Locomotion: A Prototype via Terrain Authoring and Active Learning

2022/08/16 by Chong Zhang, Zhang, Chong, Yang, Lizhi · 1 citation
Computer Science · Engineering · #FOS: Computer and information sciences #Graphics (cs.GR) #Machine Learning (cs.LG) #Robotic Locomotion and Control #Robotics (cs.RO) #Software Testing and Debugging Techniques

paper · pdf · doi:10.48550/arxiv.2208.07681

openalex publication_date 2022/08/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

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• Generating Diverse Challenging Terrains for Legged Robots Using Quality-Diversity Algorithm

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