Uploaded April 2025 | Updated September 2026, 1 week ago
No matter the vehicle, traditional control systems struggle when unexpected challenges—like damage, unforeseen environments, or new missions—push them beyond their design limits. Our Learning Introspective Control (LINC) program aims to fundamentally improve the safety of mechanical systems, such as ground vehicles, ships, and robotics, using various machine learning methods that require minimal computing power. By monitoring a platform’s real-time behavior through onboard sensors, LINC ensures stability, control, and trust—even when the unexpected happens. With our performers at Aurora Flight Sciences and MIT Department of Mechanical Engineering (MechE), we tested the latest LINC technology advances onboard a vessel to simulate its response to different disturbances and underway replenishment scenarios on the Charles River in Boston. Learn more: https://www.darpa.mil/research/programs/learning-introspective-control
No matter the vehicle, traditional control systems struggle when unexpected challenges—like damage, unforeseen environments, or new missions—push them beyond their design limits. Our Learning Introspective Control (LINC) program aims to fundamentally improve the safety of mechanical systems, such as ground vehicles, ships, and robotics, using various machine learning methods that require minimal computing power. By monitoring a platform’s real-time behavior through onboard sensors, LINC ensures stability, control, and trust—even when the unexpected happens. With our performers at Aurora Flight Sciences and MIT Department of Mechanical Engineering (MechE), we tested the latest LINC technology advances onboard a vessel to simulate its response to different disturbances and underway replenishment scenarios on the Charles River in Boston. Learn more: https://www.darpa.mil/research/programs/learning-introspective-control
