# CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in Robotics

arXiv: https://arxiv.org/abs/2308.00199 · HTML: https://mlsanity.com/paper/2308.00199/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 50 references that are in the map.
- First posted on arXiv 31 Jul 2023.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [iCaRL: Incremental Classifier and Representation Learning](https://mlsanity.com/paper/1611.07725/) (2016)
- [Large Scale Incremental Learning](https://mlsanity.com/paper/1905.13260/) (2019)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [End-to-End Incremental Learning](https://mlsanity.com/paper/1807.09536/) (2018)
- [Dynamic Few-Shot Visual Learning without Forgetting](https://mlsanity.com/paper/1804.09458/) (2018)
- [Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2004.10956/) (2020)
- [Learning without Memorizing](https://mlsanity.com/paper/1811.08051/) (2018)
- [Few-Shot Incremental Learning with Continually Evolved Classifiers](https://mlsanity.com/paper/2104.03047/) (2021)
- [Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning](https://mlsanity.com/paper/1904.03137/) (2019)
- [FearNet: Brain-Inspired Model for Incremental Learning](https://mlsanity.com/paper/1711.10563/) (2017)
- [Incremental Few-Shot Learning with Attention Attractor Networks](https://mlsanity.com/paper/1810.07218/) (2018)
- [EEC: Learning to Encode and Regenerate Images for Continual Learning](https://mlsanity.com/paper/2101.04904/) (2021)
- [Few-Shot Continual Active Learning by a Robot](https://mlsanity.com/paper/2210.04137/) (2022)
- [Tell me what this is: Few-Shot Incremental Object Learning by a Robot](https://mlsanity.com/paper/2008.00819/) (2020)
- [F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object Learning](https://mlsanity.com/paper/2103.12242/) (2021)
- [Storing Encoded Episodes as Concepts for Continual Learning](https://mlsanity.com/paper/2007.06637/) (2020)
- [What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper](https://mlsanity.com/paper/2009.05105/) (2020)
- [Incremental Learning for Robot Perception through HRI](https://mlsanity.com/paper/1701.04693/) (2017)

## Built on by (most cited first, 2 of 2)

- [Never-Ending Behavior-Cloning Agent for Robotic Manipulation](https://mlsanity.com/paper/2403.00336/) (2024)
- [Sustainable Self-evolution Adversarial Training](https://mlsanity.com/paper/2412.02270/) (2024)

## Abstract (arXiv)

For most real-world applications, robots need to adapt and learn continually with limited data in their environments. In this paper, we consider the problem of Few-Shot class Incremental Learning (FSIL), in which an AI agent is required to learn incrementally from a few data samples without forgetting the data it has previously learned. To solve this problem, we present a novel framework inspired by theories of concept learning in the hippocampus and the neocortex. Our framework represents object classes in the form of sets of clusters and stores them in memory. The framework replays data generated by the clusters of the old classes, to avoid forgetting when learning new classes. Our approach is evaluated on two object classification datasets resulting in state-of-the-art (SOTA) performance for class-incremental learning and FSIL. We also evaluate our framework for FSIL on a robot demonstrating that the robot can continually learn to classify a large set of household objects with limited human assistance.