OpenAI, Elon Musk's artificial intelligence laboratory in Sand Francisco, introduced Dactyl in 2018. The robot hands tagged as the "spinner" has five fingers just like the human hands. In 2019, the Technological University of Munich in Berlin introduced H-1: a robot with sensors like human skin. Each day, we are getting closer to the future where robots live with but not replace humans. We have available sensors in the market mass-produced for different uses.

Picking things up is such a fundamental skill for robots, and robots have been picking up things for such a long time, that it's sometimes difficult to understand how challenging grasping still is. Robots that are good at grasping things usually depend on high quality sensor data along with some amount of advance knowledge about the things that they're going to be grasping. Where grasping gets really tricky is when you're trying to design a system that can use standardized (and affordable) grippers and sensors to reliably pick up almost anything, including that infinitely long tail of objects that are, for whatever reason, weird and annoying to grasp. One way around this is to design grasping hardware that uses clever tricks (like enveloping grasps or adhesives) to compensate for not really knowing the best way to pick up a given object, but this may not be a long-term sustainable approach: Solving the problem in software is much more efficient and scalable, if you can pull it off. "I've been studying robot grasping for 30 years and I'm convinced that the key to reliable robot grasping is the perception and control software, not the hardware," Ken Goldberg, a professor of robotics and director of the AUTOLAB at UC Berkeley, told us this week.

Researchers at UC Berkeley have developed a robot that can pick up awkward and unusually shaped objects. The robot learned how to grasp different objects by studying a virtual library of 10,000 3D objects and suitable grasps. When a new object is placed in front of the bot, its deep-learning system quickly figures out what grasp the arm should use. When the robot was unsure of how to grasp an object, it poked it to figure out how to better grasp it. Deep-learning software tries to mimic the activity in layers of neurons in the neocortex, which makes up 80 percent of the brain and is where thinking occurs.