Whole-body mobile manipulation is a fundamental capability for general-purpose robotic agents, requiring both coordinated control of the mobile base and manipulator and robust perception under dynamically changing viewpoints. However, existing approaches face two key challenges: strong coupling between base and arm actions complicates whole-body control optimization, and perceptual attention is often poorly allocated as viewpoints shift during mobile manipulation. We propose InCoM, an intent-driven perception and structured coordination framework for whole-body mobile manipulation. InCoM infers latent motion intent to dynamically reweight multi-scale perceptual features, enabling stage-adaptive allocation of perceptual attention. To support robust cross-modal perception, InCoM further incorporates a geometric-semantic structured alignment mechanism that enhances multimodal correspondence. On the control side, we design a decoupled coordinated flow matching action decoder that explicitly models coordinated base-arm action generation, alleviating optimization difficulties caused by control coupling. Without access to privileged perceptual information, InCoM outperforms state-of-the-art methods on three ManiSkill-HAB scenarios by 28.2%, 26.1%, and 23.6% in success rate, demonstrating strong effectiveness for whole-body mobile manipulation.

翻译：全身移动操作是通用机器人智能体的基本能力，既需要协调控制移动基座与机械臂，又要求在动态变化的视角下具备鲁棒的感知能力。然而，现有方法面临两大关键挑战：基座与手臂动作间的强耦合使全身控制优化复杂化；随着移动操作过程中视角变化，感知注意力的分配往往不佳。我们提出InCoM，一种面向全身移动操作的意图驱动感知与结构化协调框架。InCoM通过推断潜在运动意图来动态重加权多尺度感知特征，实现阶段自适应的感知注意力分配。为支持鲁棒的跨模态感知，InCoM进一步引入几何-语义结构化对齐机制，以增强多模态对应关系。在控制层面，我们设计了解耦协调流匹配动作解码器，显式建模协调的基座-手臂动作生成，从而缓解由控制耦合引起的优化困难。在未使用特权感知信息的条件下，InCoM在三个ManiSkill-HAB场景上的成功率分别超越现有最优方法28.2%、26.1%和23.6%，充分证明了其在全身移动操作任务中的卓越有效性。