  Cooking recipes are challenging to translate to robot plans as they feature
rich linguistic complexity, temporally-extended interconnected tasks, and an
almost infinite space of possible actions. Our key insight is that combining a
source of cooking domain knowledge with a formalism that captures the temporal
richness of cooking recipes could enable the extraction of unambiguous,
robot-executable plans. In this work, we use Linear Temporal Logic (LTL) as a
formal language expressive enough to model the temporal nature of cooking
recipes. Leveraging a pretrained Large Language Model (LLM), we present
Cook2LTL, a system that translates instruction steps from an arbitrary cooking
recipe found on the internet to a set of LTL formulae, grounding high-level
cooking actions to a set of primitive actions that are executable by a
manipulator in a kitchen environment. Cook2LTL makes use of a caching scheme
that dynamically builds a queryable action library at runtime. We instantiate
Cook2LTL in a realistic simulation environment (AI2-THOR), and evaluate its
performance across a series of cooking recipes. We demonstrate that our system
significantly decreases LLM API calls (-51%), latency (-59%), and cost (-42%)
compared to a baseline that queries the LLM for every newly encountered action
at runtime.
