Learning dynamic graffiti strokes with a compliant robot

Berio, Daniel; Calinon, Sylvain; Leymarie, Frederic Fol

doi:10.1109/iros.2016.7759586

Cited by 35 publications

(28 citation statements)

References 28 publications

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“…e trajectories generated by our method re ect physiologically plausible dynamics, which can be exploited to generate realistic animations as well as to drive the smooth motion of a robotic arm [4]. We apply our method for the task of trajectory stylisation with a framework inspired by the work of W.C. Wa [47], in which di erent stylisations are given by the variations of movement that follow a common geometrical structure.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Graffiti Stylisation with Stochastic Optimal Control

Berio

Calinon

Leymarie

2017

Proceedings of the 4th International Conference on Movement Computing

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We present a method for the interactive generation of stylised letters, curves and motion paths that are similar to the ones that can be observed in art forms such as gra ti and calligraphy. We de ne various stylisations of a le er form over a common geometrical structure, which is given by the spatial layout of a sparse sequence of targets. Di erent stylisations are then generated by optimising the trajectories of a dynamical system that tracks the target sequence.e evolution of the dynamical system is computed with a stochastic formulation of optimal control, in which each target is de ned probabilistically as a multivariate Gaussian. e covariance of each Gaussian explicitly de nes the variability as well as the curvilinear evolution of trajectory segments. Given this probabilistic formulation, the optimisation procedure results in a trajectory distribution rather than a single path. It is then possible to stochastically sample from the distribution an in nite number of dynamically and aesthetically consistent trajectories which mimic the variability that is typically observed in human drawing or writing. We further demonstrate how this system can be used together with a simple user interface in order to explore di erent stylisations of interactively or procedurally de ned le ers. CCS CONCEPTS•Computing methodologies →Procedural animation; •Humancentered computing →Gestural input; KEYWORDSHuman hand-writing movement modeling; procedural calligraphy; gra ti and tags generation; model predictive control; stochastic optimal control; smoothing splines; iconic and kinemic le er forms.

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Section: Discussionmentioning

confidence: 99%

Dynamic Graffiti Stylisation with Stochastic Optimal Control

Berio

Calinon

Leymarie

2017

Proceedings of the 4th International Conference on Movement Computing

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“…More recently, examples of robots capable of producing artworks can be found in the work of X. Dong et al, who adopted a Scara robot to paint stylized portraits obtained with an algorithm based on a triangle coordinate system [17], of D. Berio et al, who adopted a collaborative Baxter robot to produce graffiti strokes [18], and by D. Song et al, who presented a 7-DOF robotic pen-drawing system, that is capable of creating pen art on arbitrary surfaces [19]. Furthermore, a robot artist performing cartoon style facial portrait painting using NPR techniques has been presented in [20].…”

Section: Related Workmentioning

confidence: 99%

Non-Photorealistic Rendering Techniques for Artistic Robotic Painting

et al. 2019

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In this paper, we present non-photorealistic rendering techniques that are applied together with a painting robot to realize artworks with original styles. Our robotic painting system is called Busker Robot and it has been considered of interest in recent art fairs and international exhibitions. It consists of a six degree-of-freedom collaborative robot and a series of image processing and path planning algorithms. In particular, here, two different rendering techniques are presented and a description of the experimental set-up is carried out. Finally, the experimental results are discussed by analyzing the elements that can account for the aesthetic appreciation of the artworks.

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“…This aspiration is an exceptionally demanding problem. There are examples of work in this direction; Berio et al (2016) demonstrate a machine learning approach to compensate for loose position tracking of safety compliant actuators in the Baxter robot. The work definitively implements an adaptive capability relative to the physical characteristic of the robot.…”

Section: Drawingmentioning

confidence: 99%

Robot Poetics: Toward a materially sensitive drawing robot

O'Dowd

2018

Electronic Workshops in Computing

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This paper opens a discussion on robot poetics: an alternative perspective on the design of robotic technology. By considering robots as metaphorical constructs they may provide new knowledge on humanity. This position is explored relative to the on-going development of a drawing robot, which will behave sensitively to material aspectsboth its own physical embodiment and worldly situationto exhibit a visual art expression intrinsic to the robot.Robot poetics. Drawing robot.

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Learning dynamic graffiti strokes with a compliant robot

Cited by 35 publications

References 28 publications

Dynamic Graffiti Stylisation with Stochastic Optimal Control

Dynamic Graffiti Stylisation with Stochastic Optimal Control

Non-Photorealistic Rendering Techniques for Artistic Robotic Painting

Robot Poetics: Toward a materially sensitive drawing robot

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