# Few-Shot Continual Active Learning by a Robot

arXiv: https://arxiv.org/abs/2210.04137 · HTML: https://mlsanity.com/paper/2210.04137/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 43 references that are in the map.
- First posted on arXiv 9 Oct 2022.

## Builds on (most cited first, 28 of 28)

- [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)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (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)
- [A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks](https://mlsanity.com/paper/1807.03888/) (2018)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [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)
- [Learning Loss for Active Learning](https://mlsanity.com/paper/1905.03677/) (2019)
- [CORe50: a New Dataset and Benchmark for Continuous Object Recognition](https://mlsanity.com/paper/1705.03550/) (2017)
- [Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2004.10956/) (2020)
- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [Task-Free Continual Learning](https://mlsanity.com/paper/1812.03596/) (2018)
- [Classification-Reconstruction Learning for Open-Set Recognition](https://mlsanity.com/paper/1812.04246/) (2018)
- [ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation](https://mlsanity.com/paper/1911.11789/) (2019)
- [A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning](https://mlsanity.com/paper/2009.01797/) (2020)
- [Semantic Curiosity for Active Visual Learning](https://mlsanity.com/paper/2006.09367/) (2020)
- [EEC: Learning to Encode and Regenerate Images for Continual Learning](https://mlsanity.com/paper/2101.04904/) (2021)
- [CLEVA-Compass: A Continual Learning EValuation Assessment Compass to Promote Research Transparency and Comparability](https://mlsanity.com/paper/2110.03331/) (2021)
- [Tell me what this is: Few-Shot Incremental Object Learning by a Robot](https://mlsanity.com/paper/2008.00819/) (2020)
- [Learning to Caption Images through a Lifetime by Asking Questions](https://mlsanity.com/paper/1812.00235/) (2018)
- [F-SIOL-310: A Robotic Dataset and Benchmark for Few-Shot Incremental Object Learning](https://mlsanity.com/paper/2103.12242/) (2021)
- [What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper](https://mlsanity.com/paper/2009.05105/) (2020)
- [Learning Novel Objects Continually Through Curiosity](https://mlsanity.com/paper/2103.07758/) (2021)
- [Don't Forget to Buy Milk: Contextually Aware Grocery Reminder Household Robot](https://mlsanity.com/paper/2207.09050/) (2022)

## Built on by (most cited first, 25 of 25)

- [LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning](https://mlsanity.com/paper/2306.03310/) (2023)
- [A Comprehensive Survey of Continual Learning: Theory, Method and Application](https://mlsanity.com/paper/2302.00487/) (2023)
- [A Survey on Deep Active Learning: Recent Advances and New Frontiers](https://mlsanity.com/paper/2405.00334/) (2024)
- [Contrastive Continual Learning with Importance Sampling and Prototype-Instance Relation Distillation](https://mlsanity.com/paper/2403.04599/) (2024)
- [Never-Ending Behavior-Cloning Agent for Robotic Manipulation](https://mlsanity.com/paper/2403.00336/) (2024)
- [Accelerating Batch Active Learning Using Continual Learning Techniques](https://mlsanity.com/paper/2305.06408/) (2023)
- [Active Test-time Vision-Language Navigation](https://mlsanity.com/paper/2506.06630/) (2025)
- [Interactive Continual Learning Architecture for Long-Term Personalization of Home Service Robots](https://mlsanity.com/paper/2403.03462/) (2024)
- [Evaluating Continual Learning on a Home Robot](https://mlsanity.com/paper/2306.02413/) (2023)
- [R^3: On-device Real-Time Deep Reinforcement Learning for Autonomous Robotics](https://mlsanity.com/paper/2308.15039/) (2023)
- [Learning to Learn for Few-shot Continual Active Learning](https://mlsanity.com/paper/2311.03732/) (2023)
- [Active Learning for Continual Learning: Keeping the Past Alive in the Present](https://mlsanity.com/paper/2501.14278/) (2025)
- [PEST: Parameter Efficient Steering of Blackbox VLMs via Agentic Few-shot Alignment for Hateful Meme Moderation](https://mlsanity.com/paper/2601.04692/) (2026)
- [Class Balance Matters to Active Class-Incremental Learning](https://mlsanity.com/paper/2412.06642/) (2024)
- [Few-Shot Vision-Language Action-Incremental Policy Learning](https://mlsanity.com/paper/2504.15517/) (2025)
- [CBCL-PR: A Cognitively Inspired Model for Class-Incremental Learning in Robotics](https://mlsanity.com/paper/2308.00199/) (2023)
- [Unsupervised, Bottom-up Category Discovery for Symbol Grounding with a Curious Robot](https://mlsanity.com/paper/2404.03092/) (2024)
- [How Do Human Users Teach a Continual Learning Robot in Repeated Interactions?](https://mlsanity.com/paper/2307.00123/) (2023)
- [CUAL: Continual Uncertainty-aware Active Learner](https://mlsanity.com/paper/2412.09701/) (2024)
- [Uncertainty Quantification in Continual Open-World Learning](https://mlsanity.com/paper/2412.16409/) (2024)
- [Consistency-Driven Calibration and Matching for Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2506.19558/) (2025)
- [BOWL: A Deceptively Simple Open World Learner](https://mlsanity.com/paper/2402.04814/) (2024)
- [ReFu: Recursive Fusion for Exemplar-Free 3D Class-Incremental Learning](https://mlsanity.com/paper/2409.12326/) (2024)
- [Incremental Human-Object Interaction Detection with Invariant Relation Representation Learning](https://mlsanity.com/paper/2510.27020/) (2025)
- [TetraRL: A Self-Adaptive Runtime for On-Device Deep Reinforcement Learning Systems](https://mlsanity.com/paper/2606.13891/) (2026)

## Abstract (arXiv)

In this paper, we consider a challenging but realistic continual learning (CL) problem, Few-Shot Continual Active Learning (FoCAL), where a CL agent is provided with unlabeled data for a new or a previously learned task in each increment and the agent only has limited labeling budget available. Towards this, we build on the continual learning and active learning literature and develop a framework that can allow a CL agent to continually learn new object classes from a few labeled training examples. Our framework represents each object class using a uniform Gaussian mixture model (GMM) and uses pseudo-rehearsal to mitigate catastrophic forgetting. The framework also uses uncertainty measures on the Gaussian representations of the previously learned classes to find the most informative samples to be labeled in an increment. We evaluate our approach on the CORe-50 dataset and on a real humanoid robot for the object classification task. The results show that our approach not only produces state-of-the-art results on the dataset but also allows a real robot to continually learn unseen objects in a real environment with limited labeling supervision provided by its user.