CoBrush: A Hierarchical Planning Framework for Human-Robot Co-Painting

Abstract:Embodied co-painting requires a robot to repeatedly update a shared physical canvas while human intent evolves over interaction. Existing reference-driven painters or reactive assistants are typically optimized for single-shot rendering or sketch completion, limiting their ability to sustain coherent multi-round collaboration or to construct complex, content-rich scenes over time. We present CoBrush, a hierarchical framework that formulates multi-round co-painting as a coordinated semantic, spatial, and execution process. By separating high-level intent inference from spatial grounding and stroke-level control, the system supports progressive scene development on real acrylic canvases. We evaluate the framework through real human-robot painting sessions, stress tests, and user studies. Compared to single-turn baselines, our approach achieves stronger semantic alignment, more stable spatial progression, and higher perceived plausibility of robot actions. These results demonstrate that structured multi-stage reasoning improves the coherence and robustness of interactive painting and supports the progressive development of content-rich physical artworks.

Robotics (cs.RO)

arXiv:2609.35476 [cs.RO]

(or arXiv:2609.35476v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.35476 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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