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This paper details a novel robotic system that leverages the Gemini Vision API to transform real-time video inputs into minimalist line drawings executed by a collaborative manipulator. The core innovation lies in integrating computer vision and neural artistic abstraction with graph-based path planning to optimize stroke order for smooth physical execution. While robotics is involved, the central contribution relies on AI-driven perception and generation capabilities rather than purely mechanical advancements or control theory alone.
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GeminiPainter's sequence-formed pipeline comprised of perception, cognition, planning, and action stages

arXiv1 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2608.00829v1 Announce Type: new Abstract: We present an autonomous robotic portrait-generation system combining real-time face detection, AI-based sketch generation, and robotic drawing. The system captures video frames, extracts facial regions, converts them into minimalist single-line sketches using the Gemini Vision API, optimizes stroke order through graph-based path planning, and executes smooth trajectories on a 6-DoF collaborative manipulator. This perception-cognition-action pipeline integrates computer vision, neural artistic abstraction, motion optimization, and robot control. User ratings on a 5-point scale were high for sketch quality 4.33, perceived execution 4.53, and user experience 4.65, indicating recognizable, appealing, and engaging robotic portraits.

Abstract:We present an autonomous robotic portrait-generation system combining real-time face detection, AI-based sketch generation, and robotic drawing. The system captures video frames, extracts facial regions, converts them into minimalist single-line sketches using the Gemini Vision API, optimizes stroke order through graph-based path planning, and executes smooth trajectories on a 6-DoF collaborative manipulator. This perception-cognition-action pipeline integrates computer vision, neural artistic abstraction, motion optimization, and robot control. User ratings on a 5-point scale were high for sketch quality 4.33, perceived execution 4.53, and user experience 4.65, indicating recognizable, appealing, and engaging robotic portraits.

Robotics (cs.RO)

arXiv:2608.00829 [cs.RO]

(or arXiv:2608.00829v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2608.00829 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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