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. This raises a natural question: Can pretrained WBT models transfer across embodiments with minimal adaptation? To answer this question, we propose Any2Any, a paradigm that efficiently transfers an existing WBT specialist to a new humanoid embodiment with only a small amount of data and compute. Any2Any first performs kinematic alignment between source and target humanoids, aligning their input and output spaces so that the pretrained source policy can be meaningfully reused on the target embodiment.Any2Any then performs dynamics adaptation by applying lightweight parameter-efficient fine-tuning (PEFT) components to selected dynamics-sensitive modules, preserving useful behavioral priors while enabling targeted adaptation to the target robot. Extensive experiments on multiple humanoid platforms and pretrained backbones show that Any2Any substantially accelerates convergence and reduces training cost compared with training from scratch, while achieving competitive or superior tracking performance. Notably, using only 1% of the compute and data required for full training, Any2Any successfully transfers Sonic models pre-trained on Unitree G1 to LimX Oli and LimX Luna. These results suggest that pretrained WBT specialists can be efficiently reused across embodiments, providing a scalable path toward deploying humanoid whole-body control on new robots.

翻译：全身跟踪（WBT）模型已成为人形机器人的关键基础技术，使其能够以高保真度模仿多样化的运动。从头训练此类模型需要大规模数据和计算资源，导致在新的人形平台上快速部署成本高昂。这自然引发一个问题：预训练的WBT模型能否通过最小化适配过程实现跨实体迁移？为解决该问题，我们提出Any2Any范式——仅需少量数据和计算即可将现有WBT专家高效迁移至新的人形实体。Any2Any首先对源实体和目标实体进行运动学对齐，通过统一其输入输出空间，使得预训练的源策略可在目标实体上实现有意义的复用。继而进行动力学适配：在选定的动力学敏感模块上应用轻量级参数高效微调（PEFT）组件，在不破坏有用行为先验的同时实现针对目标机器人的定向适配。在多个人形平台与预训练主干上的大量实验表明：与从头训练相比，Any2Any显著加速收敛并降低训练成本，同时实现具有竞争力乃至更优的跟踪性能。值得注意的是，仅需完整训练所需1%的计算量与数据量，Any2Any即可将在Unitree G1上预训练的Sonic模型成功迁移至LimX Oli与LimX Luna。这些结果表明：预训练的WBT专家可通过高效复用实现跨实体迁移，为在新机器人上部署人形全身控制提供可扩展路径。