The authors introduce CoorDex, a learning pipeline that enables high-degree-of-freedom dexterous loco-manipulation on moving humanoids by converting body and hand control into coordinated latent residual control. This approach allows the Unitree G1 humanoid to perform complex tasks like non-stop bottle grasping and fridge door opening while in motion.
- CoorDex trains privileged motion tracking teachers from simulated demonstrations and distills them into proprioception-conditioned latent priors.
- A coordinated latent residual policy composes these priors through shared task context and separate body-hand residual heads.
- The system enables the Unitree G1 with a 20-DoF WUJI hand to execute dexterous manipulation without stopping.
- Ablations show that joint-space PPO, joint-space hand control, and monolithic latent prediction fail under the same reward budget.
The method makes high-dimensional contact-rich loco-manipulation trainable by preserving natural whole-body motion while improving finger-level contact reliability.