Natural Language to LTL Translation for Non-Markovian Robot Tasks

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Solution Overview

Problem

Existing robotics approaches fail to effectively map open-ended natural language commands to Linear Temporal Logic (LTL) expressions, which are necessary for representing complex robot behaviors, due to the rich underlying semantics of LTL and the need for low-level programming knowledge from non-expert users.

Innovation Solution

Employing neural sequence-to-sequence learning models to infer LTL sequences corresponding to natural language commands, using a probabilistic variant of LTL as a goal specification language for Markov Decision Processes (MDPs) and leveraging recurrent neural networks (RNNs) with attention mechanisms to translate between English commands and LTL expressions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If Linear Temporal Logic (LTL) is used to express complex robot behaviors, then the precision and expressiveness of task specifications are improved, but the ease of operation deteriorates because non-expert users cannot express arbitrarily complex behaviors via LTL

Engineering Contradiction:

Improveprecision of task specificationVSAvoidease of expressing task specifications

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The patent introduces natural language as an intermediary layer between users and LTL. Instead of requiring users to directly write LTL formulas, they can express tasks in natural language, which is then automatically translated into LTL by a neural translation model. This mediator preserves the precision of LTL while eliminating the complexity barrier for non-expert users.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent replaces the manual mechanical process of constructing LTL formulas with an automated neural translation system. The system uses sequence-to-sequence models to automatically convert natural language input into LTL output, substituting the manual intellectual effort with an automated computational process that handles the complex translation task.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Ease of operation

If natural language is used for task specification, then the ease of operation is improved, but the ability to accurately represent complex LTL semantics deteriorates

Engineering Contradiction:

Improveease of expressing task specificationsVSAvoidaccuracy of LTL semantics representation

Core Design Contradiction:

Ease of operationVSMeasurement precision

Solution Approach 1:

The patent uses natural language as an intermediary that users can easily interact with, while the neural translation model acts as a second intermediary that bridges natural language and LTL. This two-stage intermediary approach allows users to benefit from natural language's ease of use while ensuring accurate LTL semantics are preserved through the automated translation process.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system incorporates feedback mechanisms where the translation model is trained on parallel corpora of natural language and LTL, continuously improving its ability to accurately represent LTL semantics. The feedback loop allows the system to learn from errors and refine its translation accuracy over time, ensuring that complex LTL semantics are preserved despite the use of natural language input.

Inventive Principle:

Principle #23Feedback

3Device complexity

If existing approaches map natural language to action sequences or goal states, then the simplicity of the approach is improved, but the ability to handle non-Markovian tasks deteriorates

Engineering Contradiction:

Improvesimplicity of language understanding approachVSAvoidability to handle non-Markovian tasks

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The patent introduces LTL as an intermediary representation that captures the temporal and logical structure of non-Markovian tasks. By translating natural language into LTL first, the system preserves complex temporal dependencies and constraints that would be lost in direct action sequence or goal state representations. This intermediary layer enables the handling of non-Markovian tasks while maintaining relative simplicity through automated translation.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The patent segments the language understanding process into distinct stages: natural language input, LTL translation, and robot execution. This segmentation allows each component to be optimized independently - the translation model handles the complexity of non-Markovian semantics, while the robot execution layer remains simple and focused on implementing the translated specifications.

Inventive Principle:

Principle #1Segmentation

Data Source

PatentUS11034019B2Sequence-to-sequence language grounding of non-Markovian task specifications

Publication Date: 2021.06.15 BROWN UNIVERSITY

AI summary

A method includes enabling a robot to learn a mapping between English language commands and Linear Temporal Logic (LTL) expressions, wherein neural sequence-to-sequence learning models are employed to infer a LTL sequence corresponding to a given natural language command.