Even robots are getting festive for this holiday season with a robotic hand that plays Jingle Bells on the piano. Scientists have created a 3D-printed robotic hand that practices it skills at piano playing.
Developed by researchers at the University of Cambridge, the hand was crafted by 3D-printing soft and rigid materials to imitate a hand’s bones and ligaments—minus some of the muscles and tendons. Although the lack of muscles limited the robotic hand’s full capacity, researchers found it still had a wide range of movement because of the mechanical design.
By utilizing this type of passive movement, the robotic hand was able to impersonate different styles of piano playing. These results could help researchers design robots that are capable of more natural movement.
“We can use passivity to achieve a wide range of movement in robots: walking, swimming or flying, for example,” said Josie Hughes from Cambridge’s Department of Engineering, the paper’s first author. “Smart mechanical design enables us to achieve the maximum range of movement with minimal control costs: we wanted to see just how much movement we could get with mechanics alone.”
By actuating the wrist, the hand could choose how it interacted with the piano, ultimately, allowing the ‘smart’ hand to determine how it functions with the environment.
“The basic motivation of this project is to understand embodied intelligence, that is, the intelligence in our mechanical body,” said Dr. Fumiya Iida, who led the research. “Our bodies consist of smart mechanical designs such as bones, ligaments, and skins that help us behave intelligently even without active brain-led control. By using the state-of-the-art 3D-printing technology to print human-like soft hands, we are now able to explore the importance of physical designs, in isolation from active control, which is impossible to do with human piano players as the brain cannot be ‘switched off’ like our robot.”
Eventually, the researchers hope to use their work to ignite the process of designing more complex robots that can perform a variety of tasks.
“We can extend this research to investigate how we can achieve even more complex manipulation tasks: developing robots which can perform medical procedures or handle fragile objects, for instance,” said Hughes. “This approach also reduces the amount of machine learning required to control the hand; by developing mechanical systems with intelligence built in, it makes control much easier for robots to learn.”
