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[2602.15733] MeshMimic: Geometry-Aware Humanoid Motion Learning through 3D Scene Reconstruction
Summary
MeshMimic introduces a novel framework for humanoid motion learning by integrating 3D scene reconstruction with motion control, enhancing robot interactions with their environments.
Why It Matters
This research addresses the limitations of existing humanoid motion synthesis frameworks that often overlook the geometric context of environments. By utilizing low-cost monocular sensors, MeshMimic enables more accessible and scalable training for humanoid robots, potentially advancing their autonomous capabilities in complex terrains.
Key Takeaways
- MeshMimic combines 3D scene reconstruction with humanoid motion learning.
- The framework improves motion-terrain interaction, reducing physical inconsistencies.
- Utilizes consumer-grade sensors for training, making it cost-effective.
- Demonstrates robust performance across diverse terrains.
- Offers a scalable path for the evolution of humanoid robots.
Computer Science > Robotics arXiv:2602.15733 (cs) [Submitted on 17 Feb 2026] Title:MeshMimic: Geometry-Aware Humanoid Motion Learning through 3D Scene Reconstruction Authors:Qiang Zhang, Jiahao Ma, Peiran Liu, Shuai Shi, Zeran Su, Zifan Wang, Jingkai Sun, Wei Cui, Jialin Yu, Gang Han, Wen Zhao, Pihai Sun, Kangning Yin, Jiaxu Wang, Jiahang Cao, Lingfeng Zhang, Hao Cheng, Xiaoshuai Hao, Yiding Ji, Junwei Liang, Jian Tang, Renjing Xu, Yijie Guo View a PDF of the paper titled MeshMimic: Geometry-Aware Humanoid Motion Learning through 3D Scene Reconstruction, by Qiang Zhang and 22 other authors View PDF HTML (experimental) Abstract:Humanoid motion control has witnessed significant breakthroughs in recent years, with deep reinforcement learning (RL) emerging as a primary catalyst for achieving complex, human-like behaviors. However, the high dimensionality and intricate dynamics of humanoid robots make manual motion design impractical, leading to a heavy reliance on expensive motion capture (MoCap) data. These datasets are not only costly to acquire but also frequently lack the necessary geometric context of the surrounding physical environment. Consequently, existing motion synthesis frameworks often suffer from a decoupling of motion and scene, resulting in physical inconsistencies such as contact slippage or mesh penetration during terrain-aware tasks. In this work, we present MeshMimic, an innovative framework that bridges 3D scene reconstruction and embodied intelligence to...