DARPAtv | Researchers Develop Missing LINC to Help Vehicles Adapt to Unknowns @DARPAtv | Uploaded March 2024 | Updated October 2024, 2 days ago.
DARPA's Learning Introspective Control (LINC) program is developing machine learning methods that show promise in making that scenario closer to reality. LINC aims to fundamentally improve the safety of mechanical systems – specifically in ground vehicles, ships, drone swarms, and robotics – using various methods that require minimal computing power. The result is an AI-powered controller the size of a cell phone. At Sandia National Laboratories' Robotic Vehicle Range, LINC researchers used U.S. Army robots as surrogates for larger vehicles to test their solutions, allowing the small vehicles to respond to obstacles in real-time. Experimentation will continue in 2024 in larger systems such as light aerial multipurpose vehicles and boats. More: https://www.darpa.mil/program/learning-introspective-control
DARPA's Learning Introspective Control (LINC) program is developing machine learning methods that show promise in making that scenario closer to reality. LINC aims to fundamentally improve the safety of mechanical systems – specifically in ground vehicles, ships, drone swarms, and robotics – using various methods that require minimal computing power. The result is an AI-powered controller the size of a cell phone. At Sandia National Laboratories' Robotic Vehicle Range, LINC researchers used U.S. Army robots as surrogates for larger vehicles to test their solutions, allowing the small vehicles to respond to obstacles in real-time. Experimentation will continue in 2024 in larger systems such as light aerial multipurpose vehicles and boats. More: https://www.darpa.mil/program/learning-introspective-control
