Classifier Confidence Feedback for Robot Data Collection
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Solution Overview
Problem
Machine learning systems face challenges in constructing accurate training datasets due to unbalanced databases and insufficient data, particularly when new categories emerge, leading to low confidence scores and inefficient data collection methods.
Innovation Solution
A system and method that utilize a hardware processor to pass data from robots to previously trained classifiers, adding data to training sets and retraining the classifiers when confidence scores are low, and optimizing navigation strategies to collect additional data from similar or dissimilar regions based on hyper-knowledge databases, to enhance data diversity and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If brute force approaches are used to navigate physical environment for collecting comprehensive datasets, then data coverage may be improved, but cost and time consumption increase significantly
Solution Approach 1:
The system uses confidence scores from the classifier as feedback to determine whether additional data collection is needed. When the classifier confidence is below threshold T, the system triggers targeted data collection from candidate regions, creating a feedback loop that adapts data collection to actual model needs rather than using fixed brute force approaches
Solution Approach 2:
Instead of collecting comprehensive data from all regions (excessive action), the system performs partial action by selectively collecting data only from candidate regions identified through hyper-knowledge database queries, focusing resources on areas most likely to improve the classifier
2Measurement precision
If more data is collected to improve classification accuracy for under-represented categories, then model accuracy improves, but data collection cost and complexity increase
Solution Approach 1:
The hyper-knowledge database serves as an intermediary that bridges the classifier and the physical data collection process. It translates classifier confidence scores into targeted navigation instructions, eliminating the need for complex brute force search strategies and simplifying the data collection process
Solution Approach 2:
The system changes the parameter of data collection strategy from fixed/comprehensive to dynamic/selective based on classifier confidence scores. When confidence C < T, the system activates targeted collection mode; when C >= T, it reduces collection activity, adapting the collection parameter to model performance needs
3Productivity
If targeted data collection from specific regions is performed, then data efficiency improves, but may miss diverse examples from other regions
Solution Approach 1:
The system performs preliminary action by querying the hyper-knowledge database to identify candidate regions before actual data collection occurs. This preliminary identification of regions with characteristic similarity ensures that targeted collection captures diverse examples while maintaining efficiency
Solution Approach 2:
The hyper-knowledge database serves multiple functions: it stores environmental characteristics, identifies candidate regions based on similarity criteria, and guides navigation decisions. This multi-functional component enables the system to achieve both targeted efficiency and broad coverage through a single infrastructure
Data Source
AI summary
Data associated with a region, acquired by a robot may be passed to a previously trained classifier. The classifier outputs a classification label L, and a confidence score C. Responsive to determining that the confidence score C is below a threshold T, the acquired data can be added to a training data set associated with the classifier, and the classifier retrained using the training data set which include at least information from the acquired data. Responsive to determining that the confidence score C is below the threshold T, at least one candidate region having characteristic similarity to the region can be identified. Responsive to determining that the confidence score C is not below the threshold T, at least one candidate region having a different characteristic from the region can be identified. The robot may be caused to acquire data associated with the candidate region.