Model Validation via Precomputed Transformations (MVPT) (MVPT)

Model Validation via Precomputed Transformations (MVPT) is a proprietary approach to ensuring the reliability of Machine Learning (ML) models deployed in Autonomous Systems (AS) operating in extreme or data-limited environments. Traditional ML validation techniques fail to account for unknown or unmodeled scenarios, leading to potential mission failures when AS encounter novel conditions. MVPT addresses this by precomputing model validation across a set of anticipated data transformations before deployment, allowing real-time validation onboard the AS with minimal computational overhead. This enables autonomous decision-making without requiring human intervention or time-intensive model retraining. Phase I funding will be used to establish the feasibility of MVPT by conducting a comprehensive literature review of ML validation and transformation techniques, developing a working prototype, and defining a Phase II test plan for full-scale simulation in a NASA-relevant scenario. The target markets for MVPT include NASA’s space exploration and Earth observation missions, the Department of Defense (DoD) for autonomous Unmanned Aerial Systems (UAS), and private-sector industries such as autonomous vehicles, healthcare AI, and industrial robotics.

MVPT directly supports NASA’s mission directives by improving the reliability and adaptability of ML-driven autonomous systems in space and planetary exploration. Key applications include: - Lunar and Martian Exploration: Ensuring autonomous navigation and hazard detection for rovers and landers operating in novel terrain conditions. - Deep Space Missions: Validating ML-driven anomaly detection for spacecraft fault diagnosis and autonomous decision-making beyond human reach. - Earth Observation: Supporting ML-based climate monitoring, disaster prediction, and remote sensing by ensuring accurate model operation under dynamic environmental conditions. - In-Space Manufacturing and Robotics: Enhancing safety and operational efficiency of robotic arms and automated manufacturing processes aboard the ISS and future lunar bases. By enabling real-time validation of ML models in extreme environments, MVPT increases mission success rates, reduces operational costs, and enhances the overall scientific yield of NASA missions. MVPT has broad commercialization potential across multiple high-stakes industries that require real-time, reliable ML validation: - Defense and Aerospace: Enhancing the autonomy of UAS and military AI systems by ensuring mission-critical ML models perform reliably under adversarial or unforeseen conditions. - Industrial Robotics: Ensuring adaptable, AI-driven manufacturing systems can respond to changing input conditions without requiring costly reprogramming. - Autonomous Vehicles: Providing a real-time validation mechanism for self-driving cars to adapt to weather, lighting, and road variations, improving safety and regulatory compliance. - Healthcare AI: Enabling robust validation of AI-driven diagnostics, medical imaging, and patient monitoring systems, reducing false positives and negatives in clinical applications. MVPT supports safer, more adaptable AI deployment across industries where system failure carries significant financial or safety risks, accelerating AI adoption in critical commercial applications.

This is a mature technology (TRL 7+) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.