TAMGen: Time-Aware Motion Generation for Goal-Conditioned Humanoid Control

Generative humanoid motion models can reach a spatial goal while still producing an inappropriate motion for the requested time. For the same spatial goal, simply speeding up or slowing down a motion to meet different time commands may compromise its physical quality, even if the endpoint remains accurate. This is because contact patterns and whole-body poses may also need to adapt to the time requirements. To address this challenge, we introduce TAMGen, a framework for time-aware motion generation and evaluation. Its generator uses flow matching with a root–pose architecture to capture the coupled effects of spatial goals and explicit time budgets, so that changing the time budget can change motion content rather than merely rescale playback speed. However, generated candidates may differ in target accuracy and physical quality. We therefore train a Motion Compatibility Evaluator (MCE) to assess complete candidates and select motions with better target accuracy and physical quality. When no duration is specified, MCE selects both a duration and a corresponding motion. Experiments show that TAMGen adjusts contact patterns and whole-body poses across different time commands. Compared with MotionBricks, TAMGen reduces root endpoint error by 81.6% on LAFAN and 84.9% on BoneSeed. Mean foot-sliding speed decreases by 48.6% on LAFAN and 31.6% on BoneSeed. Real-world experiments demonstrate real-time tracking of changing root and hand targets and recovery from external disturbances.