Grounding Complex Natural Language Commands for Temporal Tasks in Unseen Environments (2302.11649v2)

Abstract: Grounding navigational commands to linear temporal logic (LTL) leverages its unambiguous semantics for reasoning about long-horizon tasks and verifying the satisfaction of temporal constraints. Existing approaches require training data from the specific environment and landmarks that will be used in natural language to understand commands in those environments. We propose Lang2LTL, a modular system and a software package that leverages LLMs to ground temporal navigational commands to LTL specifications in environments without prior language data. We comprehensively evaluate Lang2LTL for five well-defined generalization behaviors. Lang2LTL demonstrates the state-of-the-art ability of a single model to ground navigational commands to diverse temporal specifications in 21 city-scaled environments. Finally, we demonstrate a physical robot using Lang2LTL can follow 52 semantically diverse navigational commands in two indoor environments.

• The paper presents Lang2LTL, a system using LLMs to translate natural language commands into LTL, enabling zero-shot robot navigation in unseen environments.
• Lang2LTL enables state-of-the-art zero-shot robot navigation in new environments by translating commands to LTL using LLMs and semantic embeddings.
• Lang2LTL advances autonomous navigation by enabling flexible, robust execution of complex temporal commands in dynamic, unknown environments.

Grounding Complex Natural Language Commands for Temporal Tasks in Unseen Environments

This paper presents Lang2LTL, a modular system designed to translate complex natural language commands into Linear Temporal Logic (LTL) specifications, facilitating robotic navigation in environments lacking pre-existing language data. Lang2LTL leverages LLMs to interpret and execute commands using LTL's precise formalism, enabling sophisticated adherence to temporal constraints without retraining the models for new environments.

Overview

Lang2LTL addresses shortcomings of past methodologies that demanded environment-specific training data. By offering a zero-shot approach, this system successfully grounds navigational commands across multiple environments, achieving state-of-the-art performance in grounding commands to LTL in 21 urban regions evaluated via OpenStreetMap data.

Key contributions include:

• Referring Expression Recognition: Utilizing LLMs to identify phrases in commands that correspond to landmarks or propositions.
• Referring Expression Grounding: Employing semantic embeddings from LLMs to match identified expressions with actual landmarks within new environments.
• Lifted Translation: Translating commands that substitute landmark names with placeholders into LTL specifications, further mapped to grounded LTL by replacing these placeholders with environment-specific propositions.

Evaluation

The paper thoroughly tests Lang2LTL against five generalization types: robustness to paraphrasing, substitutions, vocabulary shifts, unseen formulas, and unseen template instances. It demonstrates significant performance improvements over previous methods such as CopyNet and RNN-Attn, particularly in zero-shot settings, where Lang2LTL successfully navigated cities and indoor environments without prior exposure to those areas.

Results and Future Directions

Lang2LTL's evaluation indicates exceptional performance in grounding commands with diverse temporal instructions. However, the paper identifies limitations concerning certain syntactic structures and semantic ambiguities, offering insights for future research. Considerations include enhancing the lifted translation module's accuracy and dealing with environments containing multiple identical semantic landmarks.

Potential developments involve expanding Lang2LTL's lifted dataset for greater temporal and spatial instruction diversity, improving its embeddings for finer landmark distinction, and exploring interactive querying for dynamic environments. Lang2LTL sets a precedent in using conversational interfaces for reinforcement learning tasks, emphasizing scalability and adaptability in deploying autonomous robotic systems across unanticipated environments.

Conclusion

Lang2LTL presents a landmark direction in leveraging LLMs for real-time interpretation and execution of temporally complex navigational commands without environment-specific training, offering a flexible, robust system capable of substantial real-world application, particularly in autonomous navigation tasks. The modular architecture and grounding capabilities make it a significant advancement in robotic mission specification, highlighting promising avenues for future exploration in AI-driven automation.