Robotic sample acquisition is basically grasping.  Multi-finger robot sample grasping devices are controlled to securely pick up samples. While optimal grasps for perfectly modeled objects are known, grasping unmodeled objects, like a surface sample, is an open research problem. A major source of difficulty in robotic grasping, therefore, is the sensing of object parameters and grasp quality.  Humans combine the high information content of vision, several types of haptic/tactile sensors in the fingers, and a sophisticated learning process to grasp unknown objects.  In comparison, current robotic graspers rely on a much more limited set of sensors, particularly for measuring tactile properties. We have developed algorithms that are able to extract measurements of object stiffness, incipient slip, and the shape of the contact area between grasper and sample.  All three measurements are performed using a novel robotic grasper containing multiple cameras embedded within its soft silicone “flesh”. These measurements were shown, in this project, to correlate well with quality of grasp and grasp geometry. Future work will extend this capability towards controlling a robot end-effector. This will improve a grasp by adjusting both contact point locations and grasp forces.