  High cost and lack of reliability has precluded the widespread adoption of
dexterous hands in robotics. Furthermore, the lack of a viable tactile sensor
capable of sensing over the entire area of the hand impedes the rich, low-level
feedback that would improve learning of dexterous manipulation skills. This
paper introduces an inexpensive, modular, robust, and scalable platform -- the
DManus -- aimed at resolving these challenges while satisfying the large-scale
data collection capabilities demanded by deep robot learning paradigms. Studies
on human manipulation point to the criticality of low-level tactile feedback in
performing everyday dexterous tasks. The DManus comes with ReSkin sensing on
the entire surface of the palm as well as the fingertips. We demonstrate
effectiveness of the fully integrated system in a tactile aware task -- bin
picking and sorting. Code, documentation, design files, detailed assembly
instructions, trained models, task videos, and all supplementary materials
required to recreate the setup can be found on
https://sites.google.com/view/roboticsbenchmarks/platforms/dmanus.
