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arXiv · 2512.12842

SAGA: Open-World Mobile Manipulation via Structured Affordance Grounding

Kuan Fang·Yuxin Chen·Xinghao Zhu·Farzad Niroui·Lingfeng Sun·Jiuguang Wang
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Abstract

We present SAGA, a versatile and adaptive framework for visuomotor control that can generalize across various environments, task objectives, and user specifications. To efficiently learn such capability, our key idea is to disentangle high-level semantic intent from low-level visuomotor control by explicitly grounding task objectives in the observed environment. Using an affordance-based task representation, we express diverse and complex behaviors in a unified, structured form. By leveraging multimodal foundation models, SAGA grounds the proposed task representation to the robot's visual observation as 3D affordance heatmaps, highlighting task-relevant entities while abstracting away spurious appearance variations that would hinder generalization. These grounded affordances enable us to effectively train a conditional policy on multi-task demonstration data for whole-body control. In a unified framework, SAGA can solve tasks specified in different forms, including language instructions, selected points, and example demonstrations, enabling both zero-shot execution and few-shot adaptation. We instantiate SAGA on a quadrupedal manipulator and conduct extensive experiments across eleven real-world tasks. SAGA consistently outperforms end-to-end and modular baselines by substantial margins. Together, these results demonstrate that structured affordance grounding offers a scalable and effective pathway toward generalist mobile manipulation.

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Kuan Fang, Yuxin Chen, Xinghao Zhu, Farzad Niroui, Lingfeng Sun, Jiuguang Wang. 2025-12-14. SAGA: Open-World Mobile Manipulation via Structured Affordance Grounding. https://arxiv.org/abs/2512.12842

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