IMPASTO: モデルベース計画と学習された力学モデルを統合したロボット油絵再現
IMPASTO: Integrating Model-Based Planning with Learned Dynamics Models for Robotic Oil Painting Reproduction
ロボットが油絵の画像列から筆の軌道や力、色を推論して再現するシステムを提案。学習したピクセル力学モデルとモデル予測制御を統合し、自己対戦のみで学習する。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Yingke Wang, Hao Li, Yifeng Zhu, Hong-Xing Yu, Ken Goldberg, Li Fei-Fei, Jiajun Wu, Yunzhu Li, Ruohan Zhang
分類: cs.RO, cs.AI
原文アブストラクト
Robotic reproduction of oil paintings using soft brushes and pigments requires force-sensitive control of deformable tools, prediction of brushstroke effects, and multi-step stroke planning, often without human step-by-step demonstrations or faithful simulators. Given only a sequence of target oil painting images, can a robot infer and execute the stroke trajectories, forces, and colors needed to reproduce it? We present IMPASTO, a robotic oil-painting system that integrates learned pixel dynamics models with model-based planning. The dynamics models predict canvas updates from image observations and parameterized stroke actions; a receding-horizon model predictive control optimizer then plans trajectories and forces, while a force-sensitive controller executes strokes on a 7-DoF robot arm. IMPASTO integrates low-level force control, learned dynamics models, and high-level closed-loop planning, learns solely from robot self-play, and approximates human artists' single-stroke datasets and multi-stroke artworks, outperforming baselines in reproduction accuracy. Project website: https://impasto-robopainting.github.io/
