Meta Parameter Learning for Robot Control Efficiency
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Solution Overview
Problem
Existing systems struggle to efficiently determine when to stop learning meta parameter values for a learning model, leading to unnecessary learning and inefficiencies in robot control.
Innovation Solution
A learning device equipped with a meta parameter learning module, a generalization error evaluation module, and a learning continuation determination module, which learns meta parameter values based on training data, evaluates generalization error, and decides whether to continue learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If learning of meta parameter values continues without termination criteria, then learning accuracy may be improved, but learning time and computational resources are wasted due to unnecessary learning
Solution Approach 1:
The patent implements a feedback mechanism where the learning system continuously monitors the improvement in learning results and compares it against a predetermined threshold. When the improvement falls below the threshold, the system automatically terminates the learning process. This feedback loop enables the system to adaptively determine the optimal stopping point, preventing unnecessary learning while ensuring sufficient accuracy is achieved.
2Measurement precision
If learning of meta parameter values continues without termination criteria, then learning accuracy may be improved, but computational resources are wasted due to unnecessary learning
Solution Approach 1:
The system employs a feedback mechanism that monitors computational resource consumption and learning result improvement. When the improvement in learning accuracy falls below a predetermined threshold, the system terminates learning to prevent waste of computational resources. This ensures efficient resource utilization while maintaining sufficient learning accuracy.
3Device complexity
If a single learning model is used for multiple skills with different modules, then system complexity is reduced, but learning efficiency decreases due to inability to handle module-specific differences
Solution Approach 1:
The patent applies parameter changes by introducing meta parameters that control the behavior of the learning model across different modules. Instead of creating separate learning models for each module, the system uses a single learning model with adjustable meta parameters that adapt to different module characteristics. This approach maintains low system complexity while improving learning efficiency by enabling the model to handle module-specific differences through parameter adaptation.
Data Source
AI summary
A learning device performs learning of a value of a meta parameter based on training data. The meta parameter indicates a probability distribution in a learning model in which a value of a parameter follows the probability distribution. The training data represents input and output in the learning model. The learning device calculates an evaluation value indicating an evaluation of a generalization error of the learning model. The learning device determines, based on the evaluation value, whether or not it is necessary to continue the learning of the value of the meta parameter.