Cook2LTL: Translating Cooking Recipes to LTL Formulae using Large Language Models (2310.00163v2)

Abstract: 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 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.

• The paper introduces Cook2LTL, a system that translates complex cooking recipes into Linear Temporal Logic for robot-executable plans.
• It employs semantic parsing and a dynamic caching library to break down high-level instructions into primitive actions, significantly reducing computational costs.
• Experimental results in AI2-THOR simulations show reduced latency and API calls, highlighting the potential for scalable domestic robotic applications.

Analyzing Cook2LTL: Translating Cooking Recipes to LTL Formulae using LLMs

The paper presented addresses a complex intersection of natural language processing, robotics, and temporal logic through the development of Cook2LTL, a system designed to translate cooking recipes into Linear Temporal Logic (LTL) formulae. This framework effectively seeks to bridge the gap between human-readable cooking instructions and robot-executable plans by leveraging the capabilities of LLMs.

Problem Context and Core Approach

Cooking recipes provide a rich domain of tasks characterized by linguistic complexity, temporal dependencies, and a wide array of possible actions, posing challenges in direct translation to robot plans. The central challenge tackled in this work is to extract unambiguous, robot-executable tasks from such natural instructions. The authors propose using LTL as a formal language that can adequately express the temporal intricacies of recipes. Cook2LTL harnesses a pretrained LLM to decompose high-level cooking instructions into primitive actions executable by kitchen robots.

System Components

The Cook2LTL architecture consists of several key components:

1. Semantic Parsing: Semantic parsing is used to convert instructions into parametric function representations. This involves categorizing verbs, objects, actions, and associated parameters such as time and temperature from recipe steps.
2. Action Reduction: Through LLM prompting, high-level actions that are not directly executable are broken down into sequences of primitive actions. An innovative caching and queryable action library is dynamically built during runtime, enhancing system efficiency by minimizing repeated LLM calls.
3. LTL Translation: Once actions are translated into primitive sequences, they are further converted into LTL formulae. This aids in creating executable sequences that precisely capture the inherent order and dependencies of actions outlined in cooking recipes.

Experimental Evaluation

The empirical evaluation of Cook2LTL is conducted through simulations in the AI2-THOR environment, focusing on a subset of recipes from the Recipe1M+ corpus. The performance of Cook2LTL is validated against variants lacking components such as caching. The results illustrate substantial improvements: a decrease in LLM API calls by 51%, latency by 59%, and overall costs by 42%. These metrics underscore the system's enhanced efficiency when incorporating the dynamic library for caching previously reduced actions.

Implications and Future Directions

The practical implications of Cook2LTL are noteworthy, as it offers a scalable approach for deploying robotic assistants in domestic environments where understanding and executing complex tasks like cooking are crucial. Theoretically, this work contributes to the expanding field of integrating LLMs with formal logic structures to automate multi-step processes with temporal dependencies.

However, the system is not without limitations. The reliance on simulated environments limits real-world applicability, noting that real-life robotics might encounter unforeseen variables absent in controlled simulations. The development of more extensive annotated datasets for refining semantic parsing accuracy is needed to bolster performance. Similarly, improving the robustness of action reduction plans remains critical to ensure fault-tolerant operations.

In conclusion, Cook2LTL presents a significant advancement in the domain of robotic planning from natural language instructions, highlighting the potential of combining domain-specific knowledge with powerful LLMs. Future research could extend this approach to a broader range of tasks and real-world applications, further enhancing the integration of AI in everyday human activities.