This article deals with robotic object grasping. Specifically, precision grasps and the strength required in the contact points between the hand and the object to obtain a good grip. We propose to acquire the data of force sensors using a data glove and learning by imitation to encode it. RGB and depth images are used to determine objects location and orientation. Several hand-object configurations are simulated, comparing the grasp quality when maximum, minimum and truncated mean are used. The variation of grasp quality obtained is small and in some cases negligible, so we can conclude that by selecting the maximum grasping strength, we achieve a well-adjusted grasp to multiple configurations. Besides, we present a low cost strength acquisition system and an image processing stage which allows calculating the location and orientation of an object.