AR2-D2:Training a Robot Without a Robot
Jiafei Duan Yi Ru Wang Mohit Shridhar Dieter Fox Ranjay Krishna
Diligently gathered human demonstrations serve as the unsung heroes
empowering the progression of robot learning. Today, demonstrations are
collected by training people to use specialized controllers, which
(tele-)operate robots to manipulate a small number of objects. By contrast, we
introduce AR2-D2: a system for collecting demonstrations which (1) does not
require people with specialized training, (2) does not require any real robots
during data collection, and therefore, (3) enables manipulation of diverse
objects with a real robot. AR2-D2 is a framework in the form of an iOS app that
people can use to record a video of themselves manipulating any object while
simultaneously capturing essential data modalities for training a real robot.
We show that data collected via our system enables the training of behavior
cloning agents in manipulating real objects. Our experiments further show that
training with our AR data is as effective as training with real-world robot
demonstrations. Moreover, our user study indicates that users find AR2-D2
intuitive to use and require no training in contrast to four other frequently
employed methods for collecting robot demonstrations.