Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification

• URL: http://arxiv.org/abs/2303.08006v2
• Date: Tue, 21 Mar 2023 03:35:10 GMT
• Title: Data-Efficient Learning of Natural Language to Linear Temporal Logic Translators for Robot Task Specification
• Authors: Jiayi Pan, Glen Chou, Dmitry Berenson
• Abstract summary: We present a learning-based approach for translating from natural language commands to specifications with very limited human-labeled training data. This is in stark contrast to existing natural-language to translators, which require large human-labeled datasets. We show that we can translate natural language commands at 75% accuracy with far less human data.
• Score: 6.091096843566857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To make robots accessible to a broad audience, it is critical to endow them with the ability to take universal modes of communication, like commands given in natural language, and extract a concrete desired task specification, defined using a formal language like linear temporal logic (LTL). In this paper, we present a learning-based approach for translating from natural language commands to LTL specifications with very limited human-labeled training data. This is in stark contrast to existing natural-language to LTL translators, which require large human-labeled datasets, often in the form of labeled pairs of LTL formulas and natural language commands, to train the translator. To reduce reliance on human data, our approach generates a large synthetic training dataset through algorithmic generation of LTL formulas, conversion to structured English, and then exploiting the paraphrasing capabilities of modern large language models (LLMs) to synthesize a diverse corpus of natural language commands corresponding to the LTL formulas. We use this generated data to finetune an LLM and apply a constrained decoding procedure at inference time to ensure the returned LTL formula is syntactically correct. We evaluate our approach on three existing LTL/natural language datasets and show that we can translate natural language commands at 75\% accuracy with far less human data ($\le$12 annotations). Moreover, when training on large human-annotated datasets, our method achieves higher test accuracy (95\% on average) than prior work. Finally, we show the translated formulas can be used to plan long-horizon, multi-stage tasks on a 12D quadrotor.

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