  We study the problem of using high-resolution tactile sensors to control the
insertion of objects in a box-packing scenario. We propose a new system based
on a tactile sensor GelSlim for the dense packing task. In this paper, we
propose an insertion strategy that leverages tactile sensing to: 1) safely
probe the box with the grasped object while monitoring incipient slip to
maintain a stable grasp on the object. 2) estimate and correct for residual
position uncertainties to insert the object into a designated gap without
disturbing the environment.
  Our proposed methodology is based on two neural networks that estimate the
error direction and error magnitude, from a stream of tactile imprints,
acquired by two GelSlim fingers, during the insertion process. The system is
trained on four objects with basic geometric shapes, which we show generalizes
to four other common objects. Based on the estimated positional errors, a
heuristic controller iteratively adjusts the position of the object and
eventually inserts it successfully without requiring prior knowledge of the
geometry of the object. The key insight is that dense tactile feedback contains
useful information with respect to the contact interaction between the grasped
object and its environment. We achieve high success rate and show that unknown
objects can be inserted with an average of 6 attempts of the probe-correct
loop. The method's ability to generalize to novel objects makes it a good fit
for box packing in warehouse automation.
