  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.
