GAN Error Detection for Robotic Task Outcome Verification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current supervisory control frameworks for robot systems, such as CRAFTSMAN, require operator interaction for verification of task outcomes and inspection, limiting their autonomous capabilities, especially in remote and hazardous environments like the lunar surface, due to the need for extensive positive and negative training data for error detection.
Innovation Solution
The GANDER system employs Generative Adversarial Networks (GANs) to perform online error detection by mapping input images to a learned positive manifold, allowing for error detection without requiring extensive negative training data, thereby enhancing the autonomous capabilities of robot systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional error detection methods are used, then error detection capability is improved, but extensive positive and negative training data is required
Solution Approach 1:
Instead of training the GAN on both positive and negative examples as in traditional error detection methods, the patent inverts the approach by training only on positive examples. The GAN learns to generate realistic positive outcomes, and any deviation from these generated positives indicates an error. This eliminates the need for extensive negative training data while maintaining error detection capability.
Solution Approach 2:
The patent uses the GAN generator to create synthetic copies of positive task outcomes. These generated positive examples serve as the reference for error detection, replacing the need for actual negative examples. The system copies the characteristics of successful outcomes through the generator and compares against these copies to detect errors.
2Measurement precision
If supervisory/operator interaction is required for verification, then task outcome verification accuracy is improved, but operator burden increases
Solution Approach 1:
The patent implements self-service by enabling the robot system to autonomously verify its own task outcomes using the GANDER system. The GAN-based error detection automatically compares actual task outcomes against generated positive examples, allowing the system to self-verify without requiring operator interaction. This reduces operator burden while maintaining verification accuracy through automated intelligent detection.
3Ease of operation
If automated error detection is implemented, then operator burden is reduced, but robust error detection capability is limited without extensive training data
Solution Approach 1:
The patent resolves this contradiction by inverting the traditional supervised learning approach. Instead of requiring extensive labeled error data to train robust detectors, the system trains only on positive examples and uses the GAN's ability to generate realistic variations to detect deviations. This enables robust automated error detection with minimal training data, reducing operator burden while maintaining reliability.
Solution Approach 2:
The patent changes the fundamental parameter of training data composition from requiring both positive and negative examples to using only positive examples. This parameter change in the training approach, combined with the GAN's generative capabilities, enables robust error detection with reduced data requirements, allowing automated operation without compromising detection reliability.
4Quantity of substance
If GAN is trained only on positive data, then training data requirement is reduced, but error detection accuracy must be maintained
Solution Approach 1:
The patent applies preliminary action by having the GAN generator pre-learn the characteristics of positive task outcomes during training. The generator creates a comprehensive representation of what successful outcomes look like across various conditions. At runtime, this pre-learned knowledge enables accurate error detection by comparing actual outcomes against the generated positives, maintaining accuracy while requiring only positive training data.
Solution Approach 2:
The patent implements feedback through the adversarial training process where the discriminator provides feedback to the generator about generated samples. This feedback loop refines the generator's ability to create realistic positive examples, ensuring that the generated positives accurately represent true positive outcomes. The feedback mechanism maintains error detection accuracy even though training uses only positive data.
Data Source
AI summary
Systems and methods leveraging machine learning models to provide error detection in robotic systems. Generative adversarial networks are utilized to provide detection of off-nominal behaviors that may be flagged for user review or cause termination of any pending operations. A positive manifold may be used to reduce overhead in building error detection systems as this reduces the amount of training data required.