  In this paper, we present an approach to tactile pose estimation from the
first touch for known objects. First, we create an object-agnostic map from
real tactile observations to contact shapes. Next, for a new object with known
geometry, we learn a tailored perception model completely in simulation. To do
so, we simulate the contact shapes that a dense set of object poses would
produce on the sensor. Then, given a new contact shape obtained from the sensor
output, we match it against the pre-computed set using the object-specific
embedding learned purely in simulation using contrastive learning.
  This results in a perception model that can localize objects from a single
tactile observation. It also allows reasoning over pose distributions and
including additional pose constraints coming from other perception systems or
multiple contacts. We provide quantitative results for four objects. Our
approach provides high accuracy pose estimations from distinctive tactile
observations while regressing pose distributions to account for those contact
shapes that could result from different object poses. We further extend and
test our approach in multi-contact scenarios where several tactile sensors are
simultaneously in contact with the object. Website:
http://mcube.mit.edu/research/tactile_loc_first_touch.html
