Online Object Localization in a Robotic Hand by Tactile Sensing

Robotic grasping and manipulation mainly rely on vision and tactile sensing. While tactile sensors are frequently proposed for grasp control in the literature, object localization and recognition are typically achieved through vision. Vision- based approaches perform satisfactorily in clear and structured surroundings by providing reliable sensory inputs about the object. However, their performance deteriorates when faced with object occlusion, which is typical of manipulation tasks; for instance, robotic fingers occluding the object during in- hand object manipulation. This work presents an online object pose estimation based on tactile sensing. More specifically, the proposed method finds the wrist-object transformation based on the contact-point positions between fingertips and the object, representing the minimal tactile sensor requirement. We validate our method experimentally using different object ge- ometries during in-hand manipulation tasks. The experimental results demonstrate that our proposed method outperforms the vision-based approaches during in-hand object manipulation due to its inherent robustness to object occlusion.