Manipulation of objects within a robot's hand is one of the most important challenges in achieving robot dexterity. To address this challenge, Roller Graspers use steerable rolling fingertips. The fingertips impart motions and exert forces to achieve six degree of freedom mobility and closed-loop grasp force control. The design reported here uses image processing from cameras placed inside steerable compliant rollers to track contact conditions and locations. Integration of this data into a controller enables a variety of robust in-hand manipulation capabilities. We demonstrate that the same information can be used to reconstruct object shape. In addition, we show that by converting in-hand manipulation from a discontinuous process, with fingers frequently attaching and detaching from the object surface, to a continuous process, we can implement a convergent control loop that minimizes errors that otherwise accumulate during large object motions. The difference is apparent when comparing the results of an object rotation using a discontinuous finger-gaiting approach, as would be required without rolling fingertips, to the results obtained with continuous rolling. The results suggest that hybrid rolling fingertip and finger-gaiting approaches to manipulation may be a promising future research direction.