Grounding LTL Tasks in Sub-Symbolic RL Environments for Zero-Shot Generalization
The paper introduces a method for AI agents to follow complex temporal instructions written in formal logic (LTL) using only raw visual input. The authors developed a system that simultaneously learns the mapping between symbols and pixels, allowing the agent to perform zero-shot generalization on entirely new tasks without needing any prior human labeling.