InCoM：基于意图的感知与结构化协调用于移动操作
InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation
AI总结：
InCoM通过意图驱动的感知和结构化协调框架，解决移动操作中基座与机械臂动作耦合及感知注意力分配问题，提升任务成功率。
摘要：
移动操作是通用机器人代理的基本能力，需要协调基座和机械臂的控制，并在动态变化的视角下保持鲁棒的感知。然而，现有方法面临两个关键挑战：基座与机械臂动作之间的强耦合使控制优化复杂，且在移动操作过程中视角变化时感知注意力分配不佳。我们提出InCoM，一种基于意图的感知与结构化协调框架。InCoM推断潜在的运动意图以动态重新加权多尺度感知特征，实现阶段自适应的感知注意力分配。为了支持鲁棒的跨模态感知，InCoM进一步结合了几何-语义结构对齐机制，增强多模态对应性。在控制方面，我们设计了一种解耦的协调流匹配动作解码器，明确建模协调的基座-机械臂动作生成，缓解由控制耦合引起的优化困难。实验结果表明，InCoM在三个ManiSkill-HAB场景中显著优于现有方法，分别在无特权信息的情况下实现了28.2%、26.1%和23.6%的成功率提升。此外，其有效性在现实世界移动操作任务中也得到一致验证，InCoM在现有基线之上保持了更高的成功率。
英文摘要：
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 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 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 affinity refinement mechanism that enhances multimodal correspondence. On the control side, we design a decoupled coordinated flow matching action decoder that explicitly models bidirectional base-arm interactions, alleviating optimization difficulties caused by control coupling. Under matched non-privileged observations and task-specific training data, InCoM improves mean success over DSPv2 by 33.0, 26.1, and 23.6 percentage points on SetTable, TidyHouse, and PrepareGroceries, respectively. Furthermore, its effectiveness is consistently validated in real-world mobile manipulation tasks, where InCoM maintains a superior success rate over existing baselines.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
