An Embodied Companion for Visual Storytelling
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Abstract (arXiv)
As artificial intelligence shifts from pure tool for delegation toward agentic collaboration, its use in the arts can shift beyond the exploration of machine autonomy toward synergistic co-creation. While our earlier robotic works utilized automation to distance the artist's intent from the final mark, we present Companion: an artistic apparatus that integrates a drawing robot with Large Language Models (LLMs) to re-center human-machine presence. By leveraging in-context learning and real-time tool use, the system engages in bidirectional interaction via speech and sketching. This approach transforms the robot from a passive executor into a playful co-creative partner capable of driving shared visual storytelling into unexpected aesthetic territories. To validate this collaborative shift, we employed the Consensual Assessment Technique (CAT) with a panel of seven art-world experts. Results confirm that the system produces works with a distinct aesthetic identity and professional exhibition merit, demonstrating the potential of AI as a highly capable artistic collaborator.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 29 references that are in the map. First posted on arXiv 18 Jan 2026.
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12 of 29 references.
References · 12
- 2022Generative Adversarial Networkscited by 5,603
- 2017A Neural Representation of Sketch Drawingscited by 311
- 2023Foundation Models in Robotics: Applications, Challenges, and the Futurecited by 212
- 2023What Makes Good Examples for Visual In-Context Learning?cited by 150
- 2025Gemini Robotics 1.5: Pushing the Frontier of Generalist Robots with Advanced Embodied Reasoning, Thinking, and Motion Transfercited by 86
- 2023Luminate: Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creationcited by 63
- 2024Empowering LLMs to Understand and Generate Complex Vector Graphicscited by 63
- 2024SketchAgent: Language-Driven Sequential Sketch Generationcited by 30
- 2024InCoRo: In-Context Learning for Robotics Control with Feedback Loopscited by 9
- 2023Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalitiescited by 6
- 2025AutoSketch: VLM-assisted Style-Aware Vector Sketch Completioncited by 2
- 2025LLM-enhanced Interactions in Human-Robot Collaborative Drawing with Older Adultscited by 2
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- 2025Real-Time Intuitive AI Drawing System for Collaboration: Enhancing Human Creativity through Formal and Contextual Intent Integration
- 2026Exploring a Multimodal Chatbot as a Facilitator in Therapeutic Art Activity ↗
- 2024SketcherX: AI-Driven Interactive Robotic drawing with Diffusion model and Vectorization Techniques
- 2026PainterBench: A Figural Divergent-Thinking Benchmark for Tool-Using Language Models
- 2025Advances in Artificial Intelligence: A Review for the Creative Industries
- 2025Explainability-in-Action: Enabling Expressive Manipulation and Tacit Understanding by Bending Diffusion Models in ComfyUI
- 2024Equivalence: An analysis of artists' roles with Image Generative AI from Conceptual Art perspective through an interactive installation design practice
- 2026PaintCopilot: Modeling Painting as Autonomous Artistic Continuation