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AI Research
arXiv CS.AI·May 25·8 min read
Any2Any: Efficient Cross-Embodiment Transfer for Humanoid Whole-Body Tracking
TL;DR
Training whole-body tracking (WBT) models—AI systems that enable humanoid robots to imitate human motions—from scratch requires massive data and computation, making deployment on new robot platforms expensive. Any2Any transfers pretrained WBT models to new humanoid embodiments through kinematic alignment (matching input/output spaces) and lightweight parameter-efficient fine-tuning (PEFT) on dynamics-sensitive modules.
✦ Why It Matters
Engineers can deploy pretrained humanoid control models to new robot platforms with minimal retraining, reducing development time and computational costs significantly.
Key Takeaways
- Utilize Any2Any for efficient model transfer across humanoid robots.
- Reduce training costs by leveraging pretrained WBT models.
- Implement kinematic alignment for effective model adaptation.
- Achieve high tracking performance with minimal data and compute.
How It Works
Any2Any begins by aligning the kinematics of the source and target humanoid robots, ensuring that their input and output spaces are compatible. This alignment allows the pretrained WBT model to be reused effectively.
Following this, the method applies parameter-efficient fine-tuning (PEFT) to specific modules that are sensitive to the dynamics of the new robot, enabling targeted adjustments while retaining useful behavioral patterns from the original model.