{"work":{"id":"ce8ba4f8-a3c0-4614-bfa3-c93bb2eee9af","openalex_id":null,"doi":null,"arxiv_id":"2408.14472","raw_key":null,"title":"Advancing Humanoid Locomotion: Mastering Challenging Terrains with Denoising World Model Learning","authors":null,"authors_text":"X","year":2024,"venue":"cs.RO","abstract":"Humanoid robots, with their human-like skeletal structure, are especially suited for tasks in human-centric environments. However, this structure is accompanied by additional challenges in locomotion controller design, especially in complex real-world environments. As a result, existing humanoid robots are limited to relatively simple terrains, either with model-based control or model-free reinforcement learning. In this work, we introduce Denoising World Model Learning (DWL), an end-to-end reinforcement learning framework for humanoid locomotion control, which demonstrates the world's first humanoid robot to master real-world challenging terrains such as snowy and inclined land in the wild, up and down stairs, and extremely uneven terrains. All scenarios run the same learned neural network with zero-shot sim-to-real transfer, indicating the superior robustness and generalization capability of the proposed method.","external_url":"https://arxiv.org/abs/2408.14472","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-07-10T17:17:25.667030+00:00","pith_arxiv_id":"2408.14472","created_at":"2026-05-13T19:53:11.820875+00:00","updated_at":"2026-07-10T17:17:25.667030+00:00","title_quality_ok":true,"display_title":"Advancing humanoid locomotion: Mastering challenging terrains with denoising world model learning","render_title":"Advancing humanoid locomotion: Mastering challenging terrains with denoising world model learning"},"hub":{"state":{"tier_text":"hub","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":10,"external_cited_by_count":null},"tier":"hub","role_counts":[{"context_role":"background","n":1}],"polarity_counts":[{"context_polarity":"background","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}