Classifier Subset Selection for Robot Context Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems face challenges in achieving broad contextual analysis with minimal computational resource demand, particularly in environments where multiple classifiers need to be run simultaneously.
Innovation Solution
A method of operation for a robot system that involves activating a first subset of classifiers, determining a context characterization by executing these classifiers, and then selecting and activating a second subset of classifiers based on the initial characterization, using a relational model to guide the selection process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If many classifiers are run to achieve broad environment and context analysis, then contextual analysis capability is improved, but computational resource demand increases
Solution Approach 1:
The patent divides the complete set of classifiers into multiple subsets that are activated sequentially based on context characterization. Instead of running all classifiers simultaneously, the system segments them into groups (e.g., first subset, second subset, third subset) and activates only relevant subsets based on the current environmental context, thereby reducing computational resource demand while maintaining broad contextual analysis capability.
Solution Approach 2:
The system dynamically adjusts which classifier subsets are activated based on the characterized context. The robot controller determines context characterization by executing a first subset of classifiers, then selects and activates a second subset based on that characterization. This dynamic adaptation allows the system to optimize computational resource usage according to the specific environmental situation.
2Use of energy by moving object
If a first subset of classifiers is executed to determine context characterization, then computational resources are saved, but the completeness of context information may be reduced
Solution Approach 1:
The system uses feedback from the context characterization obtained by executing the first subset of classifiers to inform the selection of the second subset. The robot controller determines the context characterization based on outputs from the first subset, then uses this information to select which additional classifiers to activate. This feedback mechanism ensures that the second subset is chosen to complement and refine the context information, maintaining completeness while optimizing resource usage.
Solution Approach 2:
The system performs preliminary context characterization using a first subset of classifiers before activating the full set. By initially executing a smaller subset to obtain preliminary context information, the system can then make informed decisions about which additional classifiers are needed, avoiding the need to run all classifiers from the start and thus saving computational resources while still achieving complete context analysis.
Data Source
AI summary
The present disclosure describes systems, robots, and methods for organizing and selecting classifiers of a library of classifiers. The classifiers of the library of classifiers can be organized in a relational model, such as a hierarchy or probability model. Instead of storing, activating, or executing the entire library of classifiers at once by a robot system, computational resource demand is reduced by executing subset of classifiers to determine context, and the determined context is used as a basis for selection of another subset of classifiers. This process can be repeated, to iteratively refine context and select more specific subsets of classifiers. A selected subset of classifiers can eventually be specific to a task to be performed by the robot system, such that the robot system can take action based on output from executing such specific classifiers.