# Open-Set Object Recognition Using Mechanical Properties During Interaction

arXiv: https://arxiv.org/abs/2311.01540 · HTML: https://mlsanity.com/paper/2311.01540/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 2 Nov 2023.

## Builds on (most cited first, 14 of 14)

- [Towards Open World Object Detection](https://mlsanity.com/paper/2103.02603/) (2021)
- [Towards Open World Recognition](https://mlsanity.com/paper/1412.5687/) (2014)
- [Open-World Semi-Supervised Learning](https://mlsanity.com/paper/2102.03526/) (2021)
- [Next Generation Reservoir Computing](https://mlsanity.com/paper/2106.07688/) (2021)
- [Robust Classification with Convolutional Prototype Learning](https://mlsanity.com/paper/1805.03438/) (2018)
- [Generalized Zero-Shot Learning via Synthesized Examples](https://mlsanity.com/paper/1712.03878/) (2017)
- [Anomaly Detection in Autonomous Driving: A Survey](https://mlsanity.com/paper/2204.07974/) (2022)
- [Towards Open-Set Object Detection and Discovery](https://mlsanity.com/paper/2204.05604/) (2022)
- [UC-OWOD: Unknown-Classified Open World Object Detection](https://mlsanity.com/paper/2207.11455/) (2022)
- [A Simple Exponential Family Framework for Zero-Shot Learning](https://mlsanity.com/paper/1707.08040/) (2017)
- [Robotic Tactile Perception of Object Properties: A Review](https://mlsanity.com/paper/1711.03810/) (2017)
- [Unconstrained Face Detection and Open-Set Face Recognition Challenge](https://mlsanity.com/paper/1708.02337/) (2017)
- [Spatio-temporal Attention Model for Tactile Texture Recognition](https://mlsanity.com/paper/2008.04442/) (2020)
- [Open-set Face Recognition for Small Galleries Using Siamese Networks](https://mlsanity.com/paper/2105.06967/) (2021)

## Abstract (arXiv)

while most of the tactile robots are operated in close-set conditions, it is challenging for them to operate in open-set conditions where test objects are beyond the robots' knowledge. We proposed an open-set recognition framework using mechanical properties to recongise known objects and incrementally label novel objects. The main contribution is a clustering algorithm that exploits knowledge of known objects to estimate cluster centre and sizes, unlike a typical algorithm that randomly selects them. The framework is validated with the mechanical properties estimated from a real object during interaction. The results show that the framework could recognise objects better than alternative methods contributed by the novelty detector. Importantly, our clustering algorithm yields better clustering performance than other methods. Furthermore, the hyperparameters studies show that cluster size is important to clustering results and needed to be tuned properly.