Throughout human history, the role played by our hands cannot be understated. From pre-historic manhandling the earliest tools, to the precision demonstrated by modern-day surgeons, this dexterity is based on a limb that comprises 27 bones and over 30 muscles, guided by perhaps the most ‘human’ of all organs: the brain.
This complexity makes a robotic hand highly challenging to control. In the world of robotics, there’s no higher level than the fine motor skills required to grasp and manipulate objects with precise speed and force.
Meanwhile, companies like Google DeepMind are pushing the boundaries of artificial intelligence (AI) and are trying to understand what machines can learn, both to broaden the spectrum of practical possibilities and to guide research. When Google DeepMind wanted to expand machine learning in the complex field of robotic hands, it came across a video of one such model learning how to complete a Rubik’s cube quickly.
Read the full article in DPA's January 2025 issue