A robot played cello in a curated concert for the Malmö Symphony Orchestra in southern Sweden.

Robotics is driving innovations across various sectors nowadays. This time, a new robot has entered the music arena to transform it. In a recent video, the robot was spotted playing the cello.

The industrial robotic arms with 3D-printed parts performed with the members of the orchestra in Sweden.

Developed by researcher and composer Fredrik Gran, the robot didn’t rely on AI tools to play cello. Instead, it was programmed using composer Jacob Muhlrad’s musical score, which was specially written for the robot.

In recent years, artificial intelligence (AI) and deep learning models have advanced rapidly, becoming easily accessible. This has enabled people, even those without specialized expertise, to perform various tasks with AI. Among these models, generative adversarial networks (GANs) stand out for their outstanding performance in generating new data instances with the same characteristics as the training data, making them particularly effective for generating images, music, and text.

GANs consist of two neural networks, namely, a generator that creates new data distributions starting from random noise, and a discriminator which checks whether the generated data distribution is “real” (matching the training data) or “fake.” As training progresses, the generator improves at generating realistic distributions, and the discriminator at identifying the generated data as fake.

GANs use a loss function to measure differences between the fake and real distributions. However, this approach can cause issues like gradient vanishing and unstable learning, directly impacting stability and efficiency. Despite considerable progress in improving GANs, including structural modifications and loss function adjustments, challenges such as gradient vanishing and mode collapse, where the generator produces a limited variety, continue to limit their applicability.