  Reward learning enables robots to learn adaptable behaviors from human input.
Traditional methods model the reward as a linear function of hand-crafted
features, but that requires specifying all the relevant features a priori,
which is impossible for real-world tasks. To get around this issue, recent deep
Inverse Reinforcement Learning (IRL) methods learn rewards directly from the
raw state but this is challenging because the robot has to implicitly learn the
features that are important and how to combine them, simultaneously. Instead,
we propose a divide and conquer approach: focus human input specifically on
learning the features separately, and only then learn how to combine them into
a reward. We introduce a novel type of human input for teaching features and an
algorithm that utilizes it to learn complex features from the raw state space.
The robot can then learn how to combine them into a reward using
demonstrations, corrections, or other reward learning frameworks. We
demonstrate our method in settings where all features have to be learned from
scratch, as well as where some of the features are known. By first focusing
human input specifically on the feature(s), our method decreases sample
complexity and improves generalization of the learned reward over a deepIRL
baseline. We show this in experiments with a physical 7DOF robot manipulator,
as well as in a user study conducted in a simulated environment.
