# Iterative Closed-Loop Motion Synthesis for Scaling the Capabilities of Humanoid Control

arXiv: https://arxiv.org/abs/2602.21599 · HTML: https://mlsanity.com/paper/2602.21599/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 36 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Builds on (most cited first, 30 of 30)

- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [MotionGPT: Human Motion as a Foreign Language](https://mlsanity.com/paper/2306.14795/) (2023)
- [RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation](https://mlsanity.com/paper/2506.18088/) (2025)
- [AI Choreographer: Music Conditioned 3D Dance Generation with AIST++](https://mlsanity.com/paper/2101.08779/) (2021)
- [MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis](https://mlsanity.com/paper/2212.04495/) (2022)
- [Perpetual Humanoid Control for Real-time Simulated Avatars](https://mlsanity.com/paper/2305.06456/) (2023)
- [BeyondMimic: From Motion Tracking to Versatile Humanoid Control via Guided Diffusion](https://mlsanity.com/paper/2508.08241/) (2025)
- [MaskedMimic: Unified Physics-Based Character Control Through Masked Motion Inpainting](https://mlsanity.com/paper/2409.14393/) (2024)
- [CLoSD: Closing the Loop between Simulation and Diffusion for multi-task character control](https://mlsanity.com/paper/2410.03441/) (2024)
- [EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild](https://mlsanity.com/paper/2308.16894/) (2023)
- [Self-Adapting Language Models](https://mlsanity.com/paper/2506.10943/) (2025)
- [Go to Zero: Towards Zero-shot Motion Generation with Million-scale Data](https://mlsanity.com/paper/2507.07095/) (2025)
- [MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understanding](https://mlsanity.com/paper/2410.21747/) (2024)
- [Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models](https://mlsanity.com/paper/2504.11054/) (2025)
- [PADL: Language-Directed Physics-Based Character Control](https://mlsanity.com/paper/2301.13868/) (2023)
- [Humanoid-VLA: Towards Universal Humanoid Control with Visual Integration](https://mlsanity.com/paper/2502.14795/) (2025)
- [Neural Categorical Priors for Physics-Based Character Control](https://mlsanity.com/paper/2308.07200/) (2023)
- [Motion-X++: A Large-Scale Multimodal 3D Whole-body Human Motion Dataset](https://mlsanity.com/paper/2501.05098/) (2025)
- [PDP: Physics-Based Character Animation via Diffusion Policy](https://mlsanity.com/paper/2406.00960/) (2024)
- [Learning from Massive Human Videos for Universal Humanoid Pose Control](https://mlsanity.com/paper/2412.14172/) (2024)
- [PARC: Physics-based Augmentation with Reinforcement Learning for Character Controllers](https://mlsanity.com/paper/2505.04002/) (2025)
- [Scaling Large Motion Models with Million-Level Human Motions](https://mlsanity.com/paper/2410.03311/) (2024)
- [From Universal Humanoid Control to Automatic Physically Valid Character Creation](https://mlsanity.com/paper/2206.09286/) (2022)
- [Being-M0.5: A Real-Time Controllable Vision-Language-Motion Model](https://mlsanity.com/paper/2508.07863/) (2025)
- [PHUMA: Physically Reliable Humanoid Locomotion Dataset](https://mlsanity.com/paper/2510.26236/) (2025)
- [Skeleton2Humanoid: Animating Simulated Characters for Physically-plausible Motion In-betweening](https://mlsanity.com/paper/2210.04294/) (2022)
- [T2M-X: Learning Expressive Text-to-Motion Generation from Partially Annotated Data](https://mlsanity.com/paper/2409.13251/) (2024)
- [HuBE: Cross-Embodiment Human-like Behavior Execution for Humanoid Robots](https://mlsanity.com/paper/2508.19002/) (2025)
- [FARM: Frame-Accelerated Augmentation and Residual Mixture-of-Experts for Physics-Based High-Dynamic Humanoid Control](https://mlsanity.com/paper/2508.19926/) (2025)

## Built on by (most cited first, 1 of 1)

- [GenTrack: Physical Alignment for Robot-Native Motion Generation and Zero-Shot Humanoid Tracking](https://mlsanity.com/paper/2608.01410/) (2026)

## Abstract (arXiv)

Physics-based humanoid control relies on training with motion datasets that have diverse data distributions. However, the fixed difficulty distribution of datasets limits the performance ceiling of the trained control policies. Additionally, the method of acquiring high-quality data through professional motion capture systems is constrained by costs, making it difficult to achieve large-scale scalability. To address these issues, we propose a closed-loop automated motion data generation and iterative framework. It can generate high-quality motion data with rich action semantics, including martial arts, dance, combat, sports, gymnastics, and more. Furthermore, our framework enables difficulty iteration of policies and data through physical metrics and objective evaluations, allowing the trained tracker to break through its original difficulty limits. On the PHC single-primitive tracker, using only approximately 1/10 of the AMASS dataset size, the average failure rate on the test set (2201 clips) is reduced by 45% compared to the baseline. Finally, we conduct comprehensive ablation and comparative experiments to highlight the rationality and advantages of our framework.