This paper presents a visually-guided grip selection based on the combination of object recognition and tactile feedback of a soft-hand exoskeleton intended for hand rehabilitation. A pre-trained neural network is used to recognize the object in front of the hand exoskeleton, which is then mapped to a suitable grip type. With the object cue, it actively assists users in performing different grip movements without calibration. In a pilot experiment, one healthy user completed four different grasp-and-move tasks repeatedly. All trials were completed within 25 seconds and only one out of 20 trials failed. This shows that automated movement training can be achieved by visual guidance even without biomedical sensors. In particular, in the private setting at home without clinical supervision, it is a powerful tool for repetitive training of daily-living activities.