Researchers from the University of California, Berkeley, in the USA, have made some progress on this front by teaching code controlling a robot arm and hand to perform three tasks: grabbing an object and placing it in a specific position; pushing an object; and pushing and pulling an object after seeing the same action performed by a human arm.
Think picking up stuff, such as a toy, and placing it on a box, pushing a little car along a table, and so on.
The technique, described in a paper out this week, has been dubbed “one-shot imitation.” And, yes, it requires a lot of training before it can start copycatting people on demand. The idea is to educate the code to the point where it can immediately recognize movements, or similar movements, from its training, and replay them.
A few thousand videos depicting a human arm and a robot arm completing movements and actions are used to prime the control software. The same actions are repeated using different backgrounds, lighting effects, objects, and human arms to increase the depth of the machine-learning model’s awareness of how the limbs generally operate, and thus increase the chances of the robot successfully imitating a person on the fly.