Learning Novel Objects Continually Through Curiosity

By Ali Ayub, Alan R. Wagner. 2021

4 citations recorded by the source corpus.

Children learn continually by asking questions about the concepts they are most curious about. With robots becoming an integral part of our society, they must also learn unknown concepts continually by asking humans questions. The paper analyzes a recent state-of-the-art approach for continual learning. The paper further develops a self-supervised technique to find most of the uncertain objects in an environment by utilizing the cluster representation of the previously learned classes. We test our approach on a benchmark dataset for continual learning on robots. Our results show that our curiosity-driven continual learning approach beats random sampling and softmax-based uncertainty sampling in terms of classification accuracy and the total number of classes learned.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Few-Shot Continual Active Learning by a Robot (2022, methodology): “A preliminary version [1, 2] of this work was presented at workshops in RoMan 2020 and ICRA 2021.”
Don’t Forget to Buy Milk: Contextually Aware Grocery Reminder Household Robot (2022, methodology): “The implementation details and hyperparameters for the SUSTAIN network were used as in [32], [33].”