Image-driven robot drawing with rapid lognormal movements
| Type of publication: | Conference paper |
| Citation: | Berio_RO-MAN_2025 |
| Publication status: | Published |
| Booktitle: | In Proc. IEEE Intl Symp. on Robot and Human Interactive Communication (Ro-Man) |
| Year: | 2025 |
| Abstract: | The democratization of cobots makes them accessible for physically producing paintings and drawings in collaboration with artists. At the same time, large deep-learning models are becoming increasingly common tools for a variety of complex image generation tasks. We present a method that combines these two advancements 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. |
| Main Research Program: | Human-AI Teaming |
| Keywords: | movement primitives, robot drawing |
| Projects: |
Idiap |
| Authors: | |
| Added by: | [UNK] |
| Total mark: | 0 |
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