ADAPT: Agile Diffusion Action Priors for Robust and Steerable Online Text-Driven Humanoid Control

Abstract:We present ADAPT, an end-to-end framework for interactive, text-conditioned humanoid whole-body control. Unlike dominant text-to-motion pipelines that generate kinematic motions for a separate tracker, ADAPT solves language control with an end-to-end closed-loop control framework, where the robot must continuously respond to changing commands while maintaining balance, natural motion, and smooth transitions. ADAPT learns a diffusion-based action prior from text-labeled humanoid state-action trajectories, enabling diverse motion skills to be directly executed from language commands. To improve long-horizon robustness and smooth prompt switching, we train a lightweight residual reinforcement learning policy on top of the frozen diffusion controller. We further show that the same diffusion policy can be reused as a steerable text-conditioned motion prior for downstream task adaptation. Experiments demonstrate robust language-grounded skill execution, smooth interactive transitions, and style-preserving downstream control.

Robotics (cs.RO)

arXiv:2609.00677 [cs.RO]

(or arXiv:2609.00677v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.00677 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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