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Any2Any enables efficient transfer of humanoid robot tracking models

By PulseAugur Editorial · [3 sources] ·

Researchers have developed Any2Any, a method for efficiently transferring whole-body tracking (WBT) models to new humanoid robots. This approach significantly reduces the data and computational resources needed for adaptation, enabling faster deployment on different robotic platforms. By first aligning kinematic properties and then fine-tuning dynamics-sensitive modules, Any2Any allows pretrained models to be reused effectively, achieving competitive performance with a fraction of the original training cost. AI

IMPACT Enables faster and more cost-effective deployment of advanced humanoid robot control systems on new hardware.

RANK_REASON The cluster contains two research papers detailing new methods and models for humanoid robot control and tracking.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Any2Any enables efficient transfer of humanoid robot tracking models

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The cluster contains two research papers detailing new methods and models for humanoid robot control and tracking.
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Ming Yang, Tao Yu, Feng Li, Hua Chen ·

    Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking

    arXiv:2605.23733v1 Announce Type: cross Abstract: Whole-body tracking (WBT) models have become a key foundation for humanoid robots, enabling them to imitate diverse motions with high fidelity. Training such models from scratch requires large-scale data and computation, making ra…

  2. arXiv cs.AI TIER_1 English(EN) · Hua Chen ·

    Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking

    Whole-body tracking (WBT) models have become a key foundation for humanoid robots, enabling them to imitate diverse motions with high fidelity. Training such models from scratch requires large-scale data and computation, making rapid deployment on new humanoid platforms costly. T…

  3. arXiv cs.CV TIER_1 English(EN) · Zhengyi Luo, Ye Yuan, Tingwu Wang, Chenran Li, Fernando Casta\~neda, Sirui Chen, Zi-Ang Cao, Jiefeng Li, David Minor, Qingwei Ben, Jinhyung Park, David Sami, Zi Wang, Xingye Da, Runyu Ding, Cyrus Hogg, Lina Song, Edy Lim, Eugene Jeong, Tairan He, Haoru X… ·

    SONIC: Supersizing Motion Tracking for Natural Humanoid Whole-Body Control

    arXiv:2511.07820v3 Announce Type: replace-cross Abstract: Despite the rise of billion-parameter foundation models trained across thousands of GPUs, similar scaling gains have not been shown for humanoid control. Current neural controllers for humanoids remain modest in size, targ…

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