Learning an Explainable Trajectory Generator Using the Automaton Generative Network

Researchers

Daniela Rus / Guy Rosman / Sertac Karaman / Minoru Araki / Igor Gilitschenski / Cristian-Ioan Vasile / Xiao Li

Departments: Dept of Electrical Engineering & Computer Science, Department of Aeronautics and Astronautics
Technology Areas: Artificial Intelligence (AI) and Machine Learning (ML) / Computer Science: Networking & Signals
Impact Areas: Advanced Materials

  • method for learning an explainable trajectory generator using an automaton generative network
    United States of America | Published application

License this technology

Interested in this technology? Connect with our experienced licensing team to initiate the process.

Sign up for technology updates

Sign up now to receive the latest updates on cutting-edge technologies and innovations.