# The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning

arXiv: https://arxiv.org/abs/1902.10363 · HTML: https://mlsanity.com/paper/1902.10363/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 11 of 11)

- [EdgeFM: Leveraging Foundation Model for Open-set Learning on the Edge](https://mlsanity.com/paper/2311.10986/) (2023)
- [Distance-based Hyperspherical Classification for Multi-source Open-Set Domain Adaptation](https://mlsanity.com/paper/2107.02067/) (2021)
- [Piecewise-Linear Manifolds for Deep Metric Learning](https://mlsanity.com/paper/2403.14977/) (2024)
- [Potential Field Based Deep Metric Learning](https://mlsanity.com/paper/2405.18560/) (2024)
- [Improving safety in physical human-robot collaboration via deep metric learning](https://mlsanity.com/paper/2302.11933/) (2023)
- [Resilience to the Flowing Unknown: an Open Set Recognition Framework for Data Streams](https://mlsanity.com/paper/2411.00876/) (2024)
- [Contrastive Learning for Cross-Domain Open World Recognition](https://mlsanity.com/paper/2203.09257/) (2022)
- [Multimorbidity Content-Based Medical Image Retrieval Using Proxies](https://mlsanity.com/paper/2211.12185/) (2022)
- [Learning Empirical Bregman Divergence for Uncertain Distance Representation](https://mlsanity.com/paper/2304.07689/) (2023)
- [Enhancing Understanding Through Wildlife Re-Identification](https://mlsanity.com/paper/2405.11112/) (2024)
- [Toward Robust Single-Photon Perception for Robots: A Condition-Aware Active Learning Approach](https://mlsanity.com/paper/2505.04376/) (2025)

## Abstract (arXiv)

State-of-the-art deep neural network recognition systems are designed for a static and closed world. It is usually assumed that the distribution at test time will be the same as the distribution during training. As a result, classifiers are forced to categorise observations into one out of a set of predefined semantic classes. Robotic problems are dynamic and open world; a robot will likely observe objects that are from outside of the training set distribution. Classifier outputs in robotic applications can lead to real-world robotic action and as such, a practical recognition system should not silently fail by confidently misclassifying novel observations. We show how a deep metric learning classification system can be applied to such open set recognition problems, allowing the classifier to label novel observations as unknown. Further to detecting novel examples, we propose an open set active learning approach that allows a robot to efficiently query a user about unknown observations. Our approach enables a robot to improve its understanding of the true distribution of data in the environment, from a small number of label queries. Experimental results show that our approach significantly outperforms comparable methods in both the open set recognition and active learning problems.