Continually Learning Prototypes: A Flexible Approach for Autonomous Robots to Learn from Limited Data in Open-World Scenarios

Continually Learning Prototypes (CLP) is a prototype-based algorithm that enables autonomous robots to learn from a continuous stream of data without catastrophic forgetting, detect and learn novel objects in few-shot settings, and adapt to open-world scenarios in a semi-supervised manner.