Autonomous grasping remains challenging as unlike humans, robots do not possess a sophisticated sensing nor delicate interaction capability with the real environment. Among other efforts that tried to close the gap between them, anthropomorphic robotic hands is the most prominent direction. However, exactly following human hand design might be unnecessary as it will significantly increase the mechanical complexity and hence make it less economically feasible. Recently, soft robotic hands, a new trend has emerged, aiming to make the design adequately complex and affordable while requiring much less effort to control. In the first part of this article, we will lay out several prominent designs in this direction and their applications in real world scenarios. Having a suitable hardware simplified the complexity of software designing. However, manually controlling the hand for one task requires a significantly large amount of time and effort and doing it repeatedly is unsurprisingly tedious. Therefore, in the second part, we will show some recent techniques for soft hand autonomous control. We start by briefly discussing the analytic methods that mainly exploit the hand dynamic information. Then, data-driven approaches will be our main focus. It is the trending research topic for soft hand grasping in recent years as it has shown a high performance when dealing with a large number of various objects.