GenTrack: Online Co-Training of Text-to-Motion Generation and Zero-Shot Humanoid Tracking

Humanoid motion trackers need diverse reference motions for training, but collecting motion data and adapting it to a robot is costly. Pretrained text-to-motion generators can expand this training set by producing new robot motion references. However, motions learned from human data can remain difficult for robots to follow accurately, even after retargeting. These references can train the tracker, whose execution feedback can in turn improve the generator. We introduce GenTrack, a unified online post-training framework that co-adapts robot-native text-to-motion generation and humanoid tracking. We first train a robot-native text-to-motion generator to initialize the motion source. then alternates two updates: generated motions augment public references for tracker training, and tracker rollouts provide execution rewards for refining the generator. A lagged tracker stays fixed within each generator phase and is refreshed between phases. Controlled comparisons with one-way updates and offline replay examine whether generation and tracking benefit from learning together. The results show that online co-training can improve tracking of unseen motions while making generated motions easier to execute. Replay comparisons further suggest that updating the motion source during training offers benefits beyond reusing a fixed generated dataset. These findings support online co-training as a way to expand motion supervision without collecting additional motion data while narrowing the gap between generated references and robot execution.