Deploying humanoid robots in real-world environments requires tightly integrating perception, locomotion, and manipulation under partial observations, dynamic changes, and frequent transitions across heterogeneous sub-tasks. Towards this challenge, we propose EgoActing, a new task that grounds high-level instructions into diverse, precise, and spatially aware humanoid actions. We instantiate it with EgoActor, a hierarchical humanoid system centered on a vision-language model that predicts egocentric textual actions from monocular RGB observations. Among the actions, parameterized movement and viewpoint actions are mapped to locomotion and head controllers, while manipulation and human-interaction actions are routed to downstream skills via lightweight parsing. Trained with broad supervision from real-world egocentric demonstrations, spatial reasoning QA, and simulated demonstrations, EgoActor makes robust, context-aware decisions with a sub-second VLM forward pass for both 8B and 4B variants, while the current cloud-deployed robot loop takes 3–8 seconds end-to-end. Extensive simulated and real-world evaluations show that EgoActor bridges abstract task planning and concrete motor execution, generalizing across diverse tasks and unseen environments. Code and models are released at https://baai-agents.github.io/RoboNoid/EgoActor/.