mlsanityData to 14 Oct 2026

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

arXiv 2208.07681 ↗First posted 16 Aug 2022cs.RO cs.GR cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 19 of its 42 references that are in the map. First posted on arXiv 16 Aug 2022.

Builds on

19 of 42 references.

References · 19

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7 more
  1. 2020Multi-expert learning of adaptive legged locomotioncited by 67
  2. 2020RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal Controlcited by 59
  3. 2019DeepGait: Planning and Control of Quadrupedal Gaits using Deep Reinforcement Learningcited by 50
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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2023Resilient Legged Local Navigation: Learning to Traverse with Compromised Perception End-to-Endcited by 23
  2. 2024Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllerscited by 14
  3. 2023Probabilistic Generative Modeling for Procedural Roundabout Generation for Developing Countriescited by 1