Robot Teaching Data Evaluation via Feedback Loops
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Solution Overview
Problem
Current methods for teaching robots to perform tasks like object gripping require extensive sample data and time, especially when dealing with variations in component positions and orientations, leading to inefficiencies and potential biases in teaching data sets, which can result in reduced robot performance due to covariate shift.
Innovation Solution
An information processing device and method that generates and evaluates teaching data sets by capturing images of robot movements based on user operations, using a teaching data execution unit, learning processing unit, and feedback information generation unit to create effective teaching data sets and provide feedback for improving robot performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If learning processing is performed using teaching data covering many regions, then robot performance is improved, but the time and effort required to create teaching data increases
Solution Approach 1:
The patent implements feedback mechanisms where the robot executes teaching data and evaluation results are provided back to identify insufficient regions. This feedback loop allows iterative improvement of teaching data quality without requiring complete coverage of all regions from the start, reducing the initial time investment while maintaining performance improvement.
Solution Approach 2:
The system performs preliminary execution of teaching data to evaluate its quality before final deployment. By executing teaching data multiple times and evaluating results, the system identifies regions that need additional coverage, allowing focused data collection only where necessary rather than uniformly covering all regions.
2Adaptability or versatility
If teaching data is created without any index, then all regions can be covered, but biased or uncovered regions are generated reducing data quality
Solution Approach 1:
The evaluation unit provides feedback on teaching data quality by identifying biased or uncovered regions through execution results. This feedback mechanism enables the system to detect and correct data quality issues, ensuring comprehensive and unbiased coverage of all necessary regions.
Solution Approach 2:
The system dynamically adjusts teaching data creation based on evaluation results. Regions that are found to be biased or uncovered receive additional teaching data, while well-covered regions receive less attention, creating a dynamic balancing act that improves overall data quality.
3Productivity
If learning processing is performed without evaluation, then processing speed is faster, but useless processing is performed and time is wasted
Solution Approach 1:
The system performs evaluation selectively rather than exhaustively on all teaching data. By identifying and focusing on insufficient regions through evaluation, the system avoids redundant processing of already adequate teaching data, reducing wasted time while maintaining learning effectiveness.
4Measurement precision
If a large number of pieces of sample data are input for learning processing, then robot accuracy is improved, but the time and effort required increases significantly
Solution Approach 1:
The system performs preliminary execution and evaluation of teaching data to identify regions that actually need additional sample data. This preliminary action prevents the unnecessary collection and processing of large amounts of sample data for regions that are already well-covered, reducing time investment while maintaining accuracy.
Solution Approach 2:
Evaluation results provide feedback on which regions require additional sample data, enabling focused data collection only where necessary. This feedback-driven approach ensures robot accuracy is improved in critical regions without the need to uniformly increase sample data across all regions.
Data Source
AI summary
Provided are a device and a method for presenting an evaluation score of teaching data and necessary teaching data to a user in an easy-to-understand manner in a configuration for performing learning processing using teaching data. A teaching data execution unit generates, as learning data, a camera-captured image corresponding to movement of a robot by a user operation based on the teaching data and movement position information of the robot, a learning processing unit executes machine learning by inputting learning data generated by the teaching data execution unit and generates a teaching data set including an image and a robot behavior rule as learning result data, a feedback information generation unit executes evaluation of teaching data by inputting the learning data generated by the teaching data execution unit and the learning result data generated by the learning processing unit, and generates and outputs numerical feedback information and visual feedback information based on an evaluation result.