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Natural language provides an accessible and expressive interface to specifylong-term tasks for robotic agents. However, non-experts are likely to specifysuch tasks with high-level instructions, which abstract over specific robotactions through several layers of abstraction. We propose that key to bridgingthis gap between language and robot actions over long execution horizons arepersistent representations. We propose a persistent spatial semanticrepresentation method, and show how it enables building an agent that performshierarchical reasoning to effectively execute long-term tasks. We evaluate ourapproach on the ALFRED benchmark and achieve state-of-the-art results, despitecompletely avoiding the commonly used step-by-step instructions.

A Persistent Spatial Semantic Representation for High-level Natural Language Instruction Execution