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.

通过利用机械特性进行开放式识别，我们提出了一个开放式识别框架，用于识别已知对象并逐步标记新对象。主要贡献是一种利用已知对象知识估计聚类中心和大小的聚类算法，与典型随机选择聚类中心和大小的算法不同。该框架通过与实际对象的交互估计机械特性进行验证，结果表明该框架比新颖性检测器提供的其他方法更好地识别对象，而且我们的聚类算法表现出更好的聚类性能。此外，超参数研究表明聚类大小对聚类结果很重要，需要适当地进行调整。