机器人仅凭画作图片，就能自动规划笔触与力度复刻油画。
IMPASTO: Integrating Model-Based Planning with Learned Dynamics Models for Robotic Oil Painting Reproduction
- 用图像动态模型预测笔触对画布的影响
- 通过闭环规划实现多步笔触轨迹与力控
- 无需人类示范，自学习复现真实油画效果
使用软刷和颜料的机器人油画复刻需要对柔性工具进行力敏感控制、预测笔触效果，并完成多步笔触规划，通常缺乏人类逐步示范或高保真模拟器。仅给定一系列目标油画图像，机器人能否推断并执行所需的笔触轨迹、作用力和颜料颜色？我们提出IMPASTO，一个将学习的像素动态模型与基于模型的规划相结合的机器人油画系统。动态模型从图像观测和参数化笔触动作中预测画布变化；滚动时域模型预测控制优化器则规划轨迹与力，力敏感控制器在7自由度机械臂上执行笔触。IMPASTO整合低层力控、学习的动态模型与高层闭环规划，仅通过机器人自博弈学习，成功逼近人类艺术家单笔触数据集与多笔触艺术作品，在复刻准确率上优于基线方法。
原文摘要 · Abstract (English)
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/
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