Lu Zhou

Experiments show that WholeBodyWAM consistently benefits from increased motion-pretraining scale, improves future-motion prediction and downstream task performance, and transfers effectively to real-world humanoid manipulation.

LLMs can significantly boost forecasting accuracy, but their effectiveness is hampered by measurement limitations and sensitivity to input changes.