Picking up a simple object is intuitive for a human, but it's much trickier for a robot. Machines directed by artificial intelligence have historically found it very difficult to navigate awkwardly or unfamiliarly placed objects, outside of carefully controlled settings. But now a new robot is learning how to navigate these awkward situations -- and teach other robots to do so too.

'Baxter' was developed by Stefanie Tellex and John Oberlin at Brown University and uses a number of cameras and infrared sensors to view unfamiliar objects from a number of different angles before attempting to pick it up.


It also picks up the objects from different angles with different grasps, and shakes whatever it's holding to ensure it has a secure grip. It's a process that takes numerous tries and several hours -- picking up the object may take dozens of attempts, followed by dozens more to ensure the object is secure. (The team used a mathematical formula to optimise this process.) But once Baxter has learned how to pick something up, it can 'teach' other robots who have the same sensors and grippers by encoding the information in a format that can be uploaded to the other robots.

Tellex says that enabling robots to manipulate objects more easily is "one of the big challenges in robotics today".

-- that can learn from large data sets, but these algorithms require data," she said in a statement. "Robot practice is a way to acquire the data that a robot needs for learning to robustly manipulate objects."

If the 300 Baxter robots currently in use were able to enable robot learning, it would be possible for them to learn to grasp a million objects in just eleven days. "By having robots share what they’ve learned, it’s possible to increase the speed of data collection by orders of magnitude," Tellex said.

Tellex says the eventual goal of the project is to give robots similar abilities to human children. Children learn to navigate the world partly through learning, much like Baxter, but also rely on previous experience to adapt their knowledge quickly and efficiently. This technique is already being explored -- as seen in the robot toddler who 'taught' itself how to stand up. "Our long term aim is to use this data to generalise to novel objects," she said.