  Embodied AI has seen steady progress across a diverse set of independent
tasks. While these varied tasks have different end goals, the basic skills
required to complete them successfully overlap significantly. In this paper,
our goal is to leverage these shared skills to learn to perform multiple tasks
jointly. We propose Atomic Skill Completion (ASC), an approach for multi-task
training for Embodied AI, where a set of atomic skills shared across multiple
tasks are composed together to perform the tasks. The key to the success of
this approach is a pre-training scheme that decouples learning of the skills
from the high-level tasks making joint training effective. We use ASC to train
agents within the AI2-THOR environment to perform four interactive tasks
jointly and find it to be remarkably effective. In a multi-task setting, ASC
improves success rates by a factor of 2x on Seen scenes and 4x on Unseen scenes
compared to no pre-training. Importantly, ASC enables us to train a multi-task
agent that has a 52% higher Success Rate than training 4 independent single
task agents. Finally, our hierarchical agents are more interpretable than
traditional black-box architectures.
