publications

We tokenize multimodal biometric data–heart rate, electrodermal activity (EDA), emotion, and motion capture–and use it to train three generative models for stroke, image and biometric synthesis. These models drive a closed perception-action loop autonomous painting robot.

As agentic AI becomes increasingly involved in creative production, documenting authorship has become critical for artists, collectors, and legal contexts. We present a patch-based framework for spatial authorship attribution within human-robot collaborative painting practice, demonstrated through a forensic case study of one human artist and one robotic system across 15 abstract paintings. Using commodity flatbed scanners and leave-one-painting-out cross-validation, the approach achieves 88.8% patch-level accuracy (86.7% painting-level via majority vote), outperforming texture-based and pretrained-feature baselines (68.0%-84.7%). For collaborative artworks, where ground truth is inherently ambiguous, we use conditional Shannon entropy to quantify stylistic overlap; manually annotated hybrid regions exhibit 64% higher uncertainty than pure paintings (p=0.003), suggesting the model detects mixed authorship rather than classification failure. The trained model is specific to this human-robot pair but provides a methodological grounding for sample-efficient attribution in data-scarce human-AI creative workflows that, in the future, has the potential to extend authorship attribution to any human-robot collaborative painting.

In this work we present AURA, a framework for synergetic human-artist painting. We developed a robot arm that collaboratively paints with a human artist. The robot has an awareness of the artist’s heartbeat through the EmotiBit sensor, which provides the arousal levels of the painter. Given the heartbeat detected, the robot decides to increase proximity to the artist’s workspace or retract. If a higher heartbeat is detected, which is associated with increased arousal in human artists, the robot will move away from that area of the canvas. If the artist’s heart rate is detected as neutral, indicating the human artist’s baseline state, the robot will continue its painting actions across the entire canvas. We also demonstrate and propose alternative robot-artist interactions using natural language and physical touch. This work combines the biometrics of a human artist to inform fluent artistic interactions.

As robotic technologies evolve, their potential in artistic creation becomes an increasingly relevant topic of inquiry. This study explores how professional abstract artists perceive and experience co-creative interactions with an autonomous painting robotic arm. Eight artists engaged in six painting sessions—three with a human partner, followed by three with the robot—and subsequently participated in semi-structured interviews analyzed through reflexive thematic analysis. Human-human interactions were described as intuitive, dialogic, and emotionally engaging, whereas human-robot sessions felt more playful and reflective, offering greater autonomy and prompting for novel strategies to overcome the system’s limitations. This work offers one of the first empirical investigations into artists’ lived experiences with a robot, highlighting the value of long-term engagement and a multidisciplinary approach to human-robot co-creation.