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：全身追踪（Whole-Body Tracking，WBT）模型已成为人形机器人的关键基础，使其能够高保真地模仿多样动作。从头训练此类模型需要大规模数据和计算资源，导致在新人形平台上的快速部署成本高昂。这引出一个自然问题：预训练的WBT模型能否以最小代价跨形态迁移？为回答此问题，我们提出Any2Any范式，该范式能以少量数据和计算资源，将现有WBT专家高效迁移至新的人形形态。Any2Any首先在源与目标人形机器人间执行运动学对齐，统一其输入与输出空间，使预训练源策略可在目标形态中有意义地复用。随后，Any2Any通过向特定动力学敏感模块施加轻量级参数高效微调（PEFT）组件，进行动力学适应，从而在保留有用行为先验的同时实现针对目标机器人的定向适配。在多人形平台与预训练骨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，同时达到具有竞争力或更优的追踪性能。值得关注的是，仅需完整训练1%的计算与数据量，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。上述结果表明，预训练WBT专家可被跨形态高效复用，为在人形新机器人上部署全身控制提供了一条可扩展路径。