Immersive VR Feedback Loop for AI Training Data Generation
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Solution Overview
Problem
The manual process of generating ground truth labels for machine sensory systems is labor-intensive and difficult to scale, requiring substantial hardware and developer time, and traditional AI training methods involve slow iterative loops with expensive equipment and significant coordination.
Innovation Solution
An immersive feedback loop is used to iteratively generate synthetic scene training data, train neural networks, and evaluate performance in a virtual reality environment, allowing users to visually inspect and correct errors, thereby improving AI applications by indicating additional training data needed to reach acceptable performance levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection and labeling processes are used, then ground truth data can be obtained for AI training, but the process becomes labor-intensive and difficult to scale
Solution Approach 1:
The patent creates virtual copies of real-world environments, objects, and scenarios in a simulated world. Instead of manually collecting and labeling real sensor data, the system generates synthetic sensor data from virtual scene graphs that replicate real-world conditions. This copying approach maintains data quality while eliminating manual labeling requirements and enabling unlimited data generation at scale.
Solution Approach 2:
The system enables self-service data generation by automatically creating labeled training data through simulation. The scene graph compiler and sensor data generator automatically produce ground truth data without human intervention, allowing the AI training process to serve itself with unlimited labeled data while reducing labor-intensive manual processes.
2Reliability
If traditional iterative AI development loops are used, then AI models can be trained and evaluated, but the process requires substantial hardware, developer time, and coordination
Solution Approach 1:
The patent replaces physical hardware prototypes and real-world field captures with virtual copies in a simulated environment. Developers can train and evaluate AI models using synthetic sensor data from virtual scenes, eliminating the need for expensive hardware prototypes and time-consuming field deployments while maintaining model reliability through realistic simulation conditions.
Solution Approach 2:
The system performs preliminary actions by pre-compiling scene graphs and generating synthetic training data before actual AI model training. This allows developers to prepare comprehensive training datasets in advance, reducing iteration time by eliminating the need for sequential real-world data collection and manual labeling during each development cycle.
3Measurement precision
If large data sets are collected for AI training and evaluation, then model accuracy improves, but data collection and management becomes more complex and resource-intensive
Solution Approach 1:
The patent generates synthetic copies of real-world sensor data through virtual scene graphs, eliminating the need to collect and manage large volumes of real data. The simulated sensor data maintains the statistical properties and realism needed for accurate AI training while simplifying data management through programmatic generation and automatic labeling.
Solution Approach 2:
The system changes the approach from collecting diverse real-world data to generating controlled synthetic data with adjustable parameters. By modifying scene graph parameters, sensor configurations, and environmental conditions programmatically, the system can generate unlimited training variations without the complexity of real data collection, storage, and management infrastructure.
Data Source
AI summary
An immersive feedback loop is disclosed for improving artificial intelligence (AI) applications used for virtual reality (VR) environments. Users may iteratively generate synthetic scene training data, train a neural network on the synthetic scene training data, generate synthetic scene evaluation data for an immersive VR experience, indicate additional training data needed to correct neural network errors indicated in the VR experience, and then generate and retrain on the additional training data, until the neural network reaches an acceptable performance level.