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 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 amo
The proliferation of various humanoid robot platforms creates a strong and immediate need for efficient methods to transfer learned skills between them, reducing costs and accelerating development.
This development significantly lowers the barriers to entry and deployment for humanoid robots, enabling faster iteration and wider commercial applications across different platforms.
The ability to efficiently transfer whole-body tracking models between different humanoid embodiments means that specialists no longer need to retrain models from scratch for each new robot design.
- · Humanoid robotics developers
- · AI model developers
- · Robot manufacturers
- · Logistics and manufacturing sectors
- · Companies heavily invested in platform-specific WBT solutions
- · High-cost, custom robotics integration firms
Rapid deployment of humanoid robots across diverse applications becomes more feasible.
Increased competition and innovation within the humanoid robotics market due to reduced development costs.
Accelerated adoption of general-purpose humanoid robots in various industries, leading to significant economic restructuring.
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Read at arXiv cs.AI