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arXiv · 2507.03166

Image-driven Robot Drawing with Rapid Lognormal Movements

Daniel Berio·Guillaume Clivaz·Michael Stroh·Oliver Deussen·Réjean Plamondon·Sylvain Calinon·Frederic Fol Leymarie
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Abstract

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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Daniel Berio, Guillaume Clivaz, Michael Stroh, Oliver Deussen, Réjean Plamondon, Sylvain Calinon, Frederic Fol Leymarie. 2025-07-03. Image-driven Robot Drawing with Rapid Lognormal Movements. https://arxiv.org/abs/2507.03166

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