Dual-Language-Model Control for Error-Resilient Autonomous Decisions
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Solution Overview
Problem
Existing artificial intelligence architectures for autonomous systems, such as robots and vehicles, suffer from poor decision-making precision due to decoupled task modules, where errors in upstream modules lead to incorrect downstream decisions.
Innovation Solution
An end-to-end data processing method using a first and second language model to integrate perception, prediction, and decision-making tasks, allowing for unified handling of various sensing requirements through interaction between the models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If task modules are decoupled with clear upstream-downstream dependencies, then interpretability and ease of development are improved, but decision-making precision deteriorates due to error propagation
Solution Approach 1:
The patent segments the decision-making process into multiple specialized modules (perception module, prediction module, decision module) that process different aspects of the control task. Each module focuses on specific functions while maintaining modular architecture for ease of development, yet they work together as an integrated system to improve overall decision-making precision.
Solution Approach 2:
The patent implements feedback mechanisms where downstream modules provide information back to upstream modules. The decision module feeds back to the prediction and perception modules, allowing error correction and iterative refinement of decisions, thus preventing error propagation while maintaining modular structure.
2Ease of repair
If task modules are decoupled with clear upstream-downstream dependencies, then ease of maintenance is improved, but control precision deteriorates due to error propagation
Solution Approach 1:
The patent segments the control system into independently maintainable modules (perception, prediction, decision) that can be updated and maintained separately, preserving ease of maintenance while working together as an integrated system to achieve high control precision.
Solution Approach 2:
Feedback loops allow downstream modules to correct errors from upstream modules, maintaining high control precision even as modules are independently maintained and updated over time.
3Measurement precision
If language models are used for end-to-end processing, then decision-making precision is improved, but computational complexity increases
Solution Approach 1:
The patent segments the language model processing into specialized modules (first language model for scenario understanding, second language model for decision-making) that handle specific tasks, reducing overall computational complexity while maintaining high decision-making precision through focused processing.
Data Source
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AI summary
A data processing method is applied to the field of artificial intelligence, and includes: obtaining scenario information based on sensor data and a first prompt text by using a first language model, where a target object is a vehicle or a robot; generating a second prompt text based on the scenario information and a current execution objective of the target object by using a second language model, where the second prompt text indicates a query for information that needs to be known by the target object when the target object completes the execution objective and that is not included in the scenario information; obtaining, based on the sensor data and the second prompt text by using the first language model, a reply text corresponding to the second prompt text; and obtaining a control instruction of the target object based on the reply text, the scenario information, and the current execution objective by using the second language model.