让机器人画画更像人，用自然手势生成流畅路径。
Image-driven Robot Drawing with Rapid Lognormal Movements
- 用对数正态模型模拟人类手部运动，优化机器人绘图轨迹。
- 结合图像目标与最短时间约束，生成可执行的机器人路径。
- 适合想让机器人画得更自然的艺术家与研究者使用。
大型图像生成与视觉模型结合可微分渲染技术，已成为生成机器人可绘制路径的强大工具。然而，这些方法常忽略人类绘画/书写的内在物理特性，即通常由灵巧的手臂/手部动作完成。考虑这一因素对视觉美感及实现更直观的人机协作至关重要。本文提出一种方法，通过在图像空间定义代价函数，实现基于梯度的自然人类手势运动优化。我们采用人类手部/手臂运动的对数正态（sigma-lognormal）模型，并对其进行适配，以配合可微分矢量图形（DiffVG）渲染器使用。通过该流程，我们展示了如何结合图像驱动目标与最小时间平滑准则，生成机器人可行的轨迹。实验涵盖合成涂鸦的生成与机器人复现，以及图像抽象应用。
原文摘要 · Abstract (English)
Large image generation and vision models, combined with differentiable rendering technologies, have become powerful tools for generating paths that can be drawn or painted by a robot. However, these tools often overlook the intrinsic physicality of the human drawing/writing act, which is usually executed with skillful hand/arm gestures. Taking this into account is important for the visual aesthetics of the results and for the development of closer and more intuitive artist-robot collaboration scenarios. We present a method that bridges this gap by enabling gradient-based optimization of natural human-like motions guided by cost functions defined in image space. To this end, we use the sigma-lognormal model of human hand/arm movements, with an adaptation that enables its use in conjunction with a differentiable vector graphics (DiffVG) renderer. We demonstrate how this pipeline can be used to generate feasible trajectories for a robot by combining image-driven objectives with a minimum-time smoothing criterion. We demonstrate applications with generation and robotic reproduction of synthetic graffiti as well as image abstraction.
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