Scruff™
A framework for building artificial intelligence systems
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OUR CORE R&D / These systems range from individual
autonomous robotic platforms to large-scale, multi-agent systems
for information management, command & control
These systems range from individual autonomous robotic platforms to large-scale, multi-agent systems for information management, command & control
Our machine learning research and development identifies patterns in data using a variety of approaches, including reinforcement learning, deep learning, anomaly detection, and cluster analysis. We use these patterns to better understand the world and to make better predictions and decisions.
A framework for building artificial intelligence systems
A predictive health and maintenance app for critical energy equipment
A socially intelligent AI coach for predicting and maximizing team performance
A system that helps AI systems navigate novel situations
A tool that strengthens trust in machine learning
An immersive environment for rapidly understanding the space domain
Autonomous learning applied to robotic swarm tactics
A system to help soldiers maintain situational awareness
An infrastructure for increasing the resilience of US Navy Ships
A system to formalize course-of-action (COA) modeling
A tool to prioritize data collection
Surveillance system providing detection of attacks targeting army installations
A new machine learning approach with probabilistic modeling
Tools that make DRL agents understandable and trustable
Causal models to explain learning
A system to better understand potential threats in space
An interactive researcher’s workbench for ATR algorithms
Supervisory HMI enabling practical autonomous robot direction
A secure neural network that protects sensitive information
Probabilistic representation of intent commitments to ensure software survival
A probabilistic programming language to simplify machine learning
A weather and climate forecasting tool for mission planning
Performance analysis tools for enterprise-deployed systems
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A framework to ensure and assess trustworthiness in sensor systems
