CoBrush：人間とロボットの協調絵画のための階層的計画フレームワーク
CoBrush: A Hierarchical Planning Framework for Human-Robot Co-Painting
人間の意図が対話を通じて変化する中で、ロボットが共有キャンバス上で多ラウンドの協調絵画を実現する階層的フレームワークを提案し、実機実験で単一ターン手法より高い意味的整合性と空間的進行の安定性を示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Dantong Qin, Yike Guo, Qinlin Liu, Alessandro Bozzon, Pan Wang
分類: cs.RO
原文アブストラクト
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.