  Grasping and manipulating a wide variety of objects is a fundamental skill
that would determine the success and wide spread adaptation of robots in homes.
Several end-effector designs for robust manipulation have been proposed but
they mostly work when provided with prior information about the objects or
equipped with external sensors for estimating object shape or size. Such
approaches are limited to many-shot or unknown objects and are prone to
estimation errors from external estimation systems. We propose an approach to
grasp and manipulate previously unseen or zero-shot objects: the objects
without any prior of their shape, size, material and weight properties, using
only feedback from tactile sensors which is contrary to the state-of-the-art.
Such an approach provides robust manipulation of objects either when the object
model is not known or when it is estimated incorrectly from an external system.
Our approach is inspired by the ideology of how animals or humans manipulate
objects, i.e., by using feedback from their skin. Our grasping and manipulation
revolves around the simple notion that objects slip if not grasped stably. This
slippage can be detected and counteracted for a robust grasp that is agnostic
to the type, shape, size, material and weight of the object. At the crux of our
approach is a novel tactile feedback based controller that detects and
compensates for slip during grasp. We successfully evaluate and demonstrate our
proposed approach on many real world experiments using the Shadow Dexterous
Hand equipped with BioTac SP tactile sensors for different object shapes,
sizes, weights and materials. We obtain an overall success rate of 73.5%
