Testing an unsupervised deep learning model for robot imitation of human motions
Robots that can closely imitate the actions and movements of humans in real-time could be incredibly useful, as they could learn to complete everyday tasks in specific ways without having to be extensively pre-programmed on these tasks. While techniques to enable imitation learning considerably improved over the past few years, their performance is often hampered by the lack of correspondence between a robot’s body and that of its human user.
One of the most promising approaches to teaching robots how to complete manual tasks such as cleaning dishes or preparing food is known as imitation learning. End-to-end imitation learning typically entails training a deep learning algorithm on raw videos, images and/or motion capture data of humans completing manual tasks.
From wiping up spills to serving up food, robots are being taught to carry out increasingly complicated household tasks. Many such home-bot trainees are learning through imitation; they are programmed to copy the motions that a human physically guides them through.