InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation

TL;DR

InCoM introduces an intent-driven perception and structured coordination framework that improves mobile manipulation by dynamically reallocating perceptual attention and decoupling control actions, leading to significant performance gains.

Contribution

The paper presents a novel framework combining intent inference, multi-scale perceptual reweighting, and decoupled control modeling to enhance mobile manipulation capabilities.

Findings

InCoM achieves success rate improvements of over 23% in ManiSkill-HAB scenarios.

The framework maintains superior real-world task success rates compared to baselines.

Experimental results validate the effectiveness of intent-driven perception and decoupled control.

Abstract

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 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. Experimental results demonstrate that InCoM significantly outperforms state-of-the-art methods, achieving success rate gains of 28.2%, 26.1%, and 23.6% across three ManiSkill-HAB scenarios without privileged information. Furthermore, its effectiveness is consistently validated in real-world mobile manipulation tasks, where InCoM maintains a superior success rate over existing baselines.