Control Data Reliability Estimation With Directional Generative Models
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Solution Overview
Problem
Existing computer-controlled systems face challenges in dealing with unexpected situations due to insufficient training data, leading to potential harmful actions, and there is a need for more accurate methods to estimate reliability and generate synthetic data for control models, especially for directional data representing orientations or directions.
Innovation Solution
A generative model using a coupling layer with rotations and circle transformations is employed to estimate reliability and generate synthetic directional data, ensuring accurate representation and efficient computation, avoiding singularities and enabling exact likelihood evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If a machine learnable control model is trained on limited training data, then training time and data collection cost are reduced, but the reliability of control data for unexpected situations deteriorates
Solution Approach 1:
The generative model is trained in advance on the available training data to learn the underlying data distribution. This preliminary action enables the model to generate synthetic directional data that reflects the characteristics of the training data, thereby improving reliability for unexpected situations without requiring additional training time
Solution Approach 2:
The generative model creates synthetic directional data instances that copy the statistical properties and patterns of the original training data. These synthetic copies augment the training dataset, allowing the control model to be trained on more diverse data without collecting additional real-world data, thus improving reliability while maintaining efficient training
2Device complexity
If standard generative models are used for directional data, then implementation is simpler, but accuracy in representing directional characteristics deteriorates
Solution Approach 1:
The model employs a coupling layer with specific parameterized transformations including rotations and circle transformations. These parameter changes enable the model to accurately represent directional data on hyperspheres by transforming coordinates in a manner that preserves angular relationships, thereby improving directional representation accuracy while maintaining computational efficiency
3Measurement precision
If complex transformations are applied to directional data, then accuracy of directional representation is improved, but computational efficiency and parallelizability deteriorate
Solution Approach 1:
The coupling layer is divided into multiple independent transformation stages: rotation transformations, circle transformations, and scaling operations. Each stage operates independently on specific coordinate pairs, allowing parallel computation across different data samples and improving computational efficiency while maintaining accurate directional representation
Solution Approach 2:
The model operates in the latent space of the generative model, transforming data in this intermediate dimension before mapping back to directional space. This dimensional transformation enables complex directional relationships to be captured through simpler linear transformations in the latent space, improving both accuracy and computational efficiency
4Object-affected harmful factors
If anomaly detection is performed to handle unexpected situations, then safety is improved, but system complexity and response time increase
Solution Approach 1:
The generative model is trained in advance to learn the distribution of normal operational data. During deployment, the model can quickly evaluate whether new directional data instances conform to the learned distribution by computing likelihood values, enabling rapid anomaly detection without complex real-time analysis systems
Solution Approach 2:
The system uses likelihood evaluation to provide feedback on whether incoming directional data conforms to the training data distribution. When the likelihood falls below a threshold, the system triggers safety protocols such as halting operation or switching to safe mode, creating a closed-loop feedback mechanism that improves safety while maintaining simple system architecture
Data Source
AI summary
A computer-implemented method of estimating a reliability of control data for a computer-controlled system interacting with an environment. The control data is inferred from a model input by a machine learnable control model which is trained on a training dataset. The model input comprises at least one direction vector which is extracted from sensor data and which is associated with a component of the computer-controlled system or an object in the environment. The reliability is estimated using a generative model that is trained to generate synthetic model inputs representative of the training dataset, by applying an inverse of the generative model to the model input to determine a likelihood of the model input being generated according to the generative model. The generative model comprises a coupling layer comprising a circle transformation and one or more of an unconditional rotation and a conditional rotation.