  Natural language provides an accessible and expressive interface to specify
long-term tasks for robotic agents. However, non-experts are likely to specify
such tasks with high-level instructions, which abstract over specific robot
actions through several layers of abstraction. We propose that key to bridging
this gap between language and robot actions over long execution horizons are
persistent representations. We propose a persistent spatial semantic
representation method, and show how it enables building an agent that performs
hierarchical reasoning to effectively execute long-term tasks. We evaluate our
approach on the ALFRED benchmark and achieve state-of-the-art results, despite
completely avoiding the commonly used step-by-step instructions.
