CoBrush：一种用于人机共绘的分层规划框架
CoBrush: A Hierarchical Planning Framework for Human-Robot Co-Painting
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中文总结 AI 辅助
CoBrush提出分层规划框架，将多轮人机共绘分解为语义、空间和执行过程，提升语义对齐、空间稳定性和动作合理性，支持复杂场景的渐进式构建。
中文摘要 AI 辅助
具身共绘要求机器人在人类意图随交互演变时，反复更新共享的物理画布。现有的参考驱动型画家或反应式助手通常针对单次渲染或草图补全进行优化，限制了它们维持连贯的多轮协作或随时间构建复杂、内容丰富场景的能力。我们提出了CoBrush，一个分层框架，将多轮共绘表述为协调的语义、空间和执行过程。通过将高层意图推断与空间定位和笔画级控制分离，该系统支持在真实丙烯酸画布上进行渐进式场景开发。我们通过真实的人机绘画会话、压力测试和用户研究来评估该框架。与单轮基线相比，我们的方法实现了更强的语义对齐、更稳定的空间进展以及更高的机器人动作感知合理性。这些结果表明，结构化的多阶段推理提高了交互绘画的连贯性和鲁棒性，并支持内容丰富物理艺术品的渐进式开发。
英文摘要
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.
发表机构
- Delft University of Technology（代尔夫特理工大学）
- Hong Kong University of Science and Technology（香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。
