  Planning at a higher level of abstraction instead of low level torques
improves the sample efficiency in reinforcement learning, and computational
efficiency in classical planning. We propose a method to learn such
hierarchical abstractions, or subroutines from egocentric video data of experts
performing tasks. We learn a self-supervised inverse model on small amounts of
random interaction data to pseudo-label the expert egocentric videos with agent
actions. Visuomotor subroutines are acquired from these pseudo-labeled videos
by learning a latent intent-conditioned policy that predicts the inferred
pseudo-actions from the corresponding image observations. We demonstrate our
proposed approach in context of navigation, and show that we can successfully
learn consistent and diverse visuomotor subroutines from passive egocentric
videos. We demonstrate the utility of our acquired visuomotor subroutines by
using them as is for exploration, and as sub-policies in a hierarchical RL
framework for reaching point goals and semantic goals. We also demonstrate
behavior of our subroutines in the real world, by deploying them on a real
robotic platform. Project website:
https://ashishkumar1993.github.io/subroutines/.
