With the increasing presence of robotic agents in our daily life, computationally efficient modelling of real-world objects by autonomous systems is of prime importance for enabling these artificial agents to automatically and effectively perform tasks such as visual object recognition. For this purpose, we introduce a novel, machine-learning approach for instance selection called Approach for Selection of Border Instances (ASBI). This method adopts the notion of local sets to select the most representative instances at the boundaries of the classes, in order to reduce the set of training instances and, consequently, to reduce the computational resources that are necessary to perform the learning process of real-world objects by the artificial agents. Our new algorithm was validated on 27 standard datasets and applied on 2 challenging object-modelling datasets to test the automated object recognition task. ASBI performances were compared to those of 6 state-of-art algorithms, considering three standard metrics, namely, accuracy, reduction, and effectiveness. All the obtained results show that the proposed method is promising for the autonomous recognition task, while presenting the best trade-off between the classification accuracy and the data size reduction.