Abstract
In this paper we propose a novel method for in-hand object recognition. The method is composed of a grasp stabilization controller and two exploratory behaviours to capture the shape and the softness of an object. Grasp stabilization plays an important role in recognizing objects. First, it prevents the object from slipping and facilitates the exploration of the object. Second, reaching a stable and repeatable position adds robustness to the learning algorithm and increases invariance with respect to the way in which the robot grasps the object. The stable poses are estimated using a Gaussian mixture model (GMM). We present experimental results showing that using our method the classifier can successfully distinguish 30 objects. We also compare our method with a benchmark experiment, in which the grasp stabilization is disabled. We show, with statistical significance, that our method outperforms the benchmark method.
DOI
10.1109/icar.2017.8023495
Publication Date
2017-07-01
Event
2017 18th International Conference on Advanced Robotics (ICAR)
Publication Title
2017 18th International Conference on Advanced Robotics (ICAR)
Publisher
IEEE
Embargo Period
2024-11-22
Recommended Citation
Regoli, M., Jamali, N., Metta, G., & Natale, L. (2017) 'Controlled tactile exploration and haptic object recognition', 2017 18th International Conference on Advanced Robotics (ICAR), . IEEE: Available at: https://doi.org/10.1109/icar.2017.8023495
