提出统一框架P³，让机器人更灵活地感知环境、用工具和规划多任务。

Embodied agents have demonstrated promising capabilities in interacting with physical environments. Yet, versatile embodied agents face three core bottlenecks: dynamic environmental perception, open tool access, and complex multi-task planning. Prior methods depend entirely on tool feedback to track scene changes and task progress, leading to poor real-time adaptability, error accumulation, and limited tool compatibility; multi-task scheduling is also understudied due to the difficulty of handling task dependencies and conflicting priorities. To address these limitations, we propose $\mathcal P^3$, a unified framework integrating real-time perception and dynamic scheduling, which perceives task-relevant information actively from the environment, plugs and utilizes tools without feedback requirements, and plans multi-task execution by prioritizing urgent tasks and dynamically adjusting task order based on dependencies. We additionally build the Active Task Perception (ATP) benchmark to quantitatively measure VLMs' capacity for active scene understanding and task proposal. Evaluations on the ATP benchmark verify that multiple VLMs can detect and propose active tasks, and comprehensive real-world robot experiments prove our method bridges the gap between benchmarks and practical deployment, yielding transferable general-purpose embodied agents. Code and data are available at https://github.com/fz-zsl/P3.