SBIR Phase I: Modular and Updatable Artificial Intelligence (AI) for Robotics

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The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide a novel recognition architecture to computer vision in the robotics industry. The project seeks to enable computer learn without rehearsal, allowing corrections for details that are present in the real world environment. The aim of this project is a solution to be used by computer vision customers to solve their problems immediately (without sending data back to retrain the whole network), reducing machine and customer downtime and disruption while increasing productivity. The initial focus is on robotics with computer vision limitations though the technology may be useful to other industries. Success in improving computer vision-based learning could facilitate disaster responses, augment current physical abilities, and enable exploration beyond the boundaries of Earth. This Small Business Innovation Research (SBIR) Phase I project will help create a framework to overcome rehearsal requirements that limit automated robots’ utility within life-like, dynamic environments. Artificial intelligence (AI) remains inflexible compared to humans at quickly accumulating knowledge without forgetting what they have previously learned. Robots using AI are currently only used in environments that are very limited and are very tightly controlled. Everything that might happen in the robot’s work environment must be included their training set. The proposed AI solution is suited for learning in dynamic environments without rehearsal while maintaining scalability as information is encountered. This technology may allow robots to be trained within their environment. This project may enable visual capabilities leading to a demonstration of flexible learning without rehearsal within dynamic robotic environments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.