  During the last decade, the paradigm of compressed sensing has gained
significant importance in the signal processing community. While the original
idea was to utilize sparsity assumptions to design powerful recovery algorithms
of vectors $x \in \mathbb{R}^d$, the concept has been extended to cover many
other types of problems. A noteable example is low-rank matrix recovery. Many
methods used for recovery rely on solving convex programs.
  A particularly nice trait of compressed sensing is its geometrical intuition.
In recent papers, a classical optimality condition has been used together with
tools from convex geometry and probability theory to prove beautiful results
concerning the recovery of signals from Gaussian measurements. In this paper,
we aim to formulate a geometrical condition for stability and robustness, i.e.
for the recovery of approximately structured signals from noisy measurements.
  We will investigate the connection between the new condition with the notion
of restricted singular values, classical stability and robustness conditions in
compressed sensing, and also to important geometrical concepts from complexity
theory. We will also prove the maybe somewhat surprising fact that for many
convex programs, exact recovery of a signal $x_0$ immediately implies some
stability and robustness when recovering signals close to $x_0$.
