This work proposes AdaPT, an Adaptive Motion Planning and Tracking framework that learns professional tennis serving and rally styles directly from broadcast videos, and improves tracking robustness by learning to track randomized execution speeds, while conditioning the planner on a learned motion-speed adapter to mit...
Tao Huang, Ruofei Liu, Xuchen Tang et al.· 0 citations
By constraining strategic exploration through a pretrained motion decoder, RoboStriker substantially reduces the catastrophic balance failures observed in raw action-space methods and achieves superior tactical performance in both competitive win rates and striking efficiency.
Kang-Ning Yin, Kaige Liu, Zhe Cao et al.· 0 citations
This work revisits the scaling recipe for BFMs and demonstrates that substantial performance gains can be achieved through the coordination of three core components: the learning paradigm of motion tracking that reformulates diverse humanoid control problems as the reproduction of integrated whole-body behaviors in the...