  In this paper, we present Tac2Pose, an object-specific approach to tactile
pose estimation from the first touch for known objects. Given the object
geometry, we learn a tailored perception model in simulation that estimates a
probability distribution over possible object poses given a tactile
observation. 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, we match it against the pre-computed set using an
object-specific embedding learned using contrastive learning. We obtain contact
shapes from the sensor with an object-agnostic calibration step that maps RGB
tactile observations to binary contact shapes. This mapping, which can be
reused across object and sensor instances, is the only step trained with real
sensor data. This results in a perception model that localizes objects from the
first real tactile observation. Importantly, it produces pose distributions and
can incorporate additional pose constraints coming from other perception
systems, contacts, or priors.
  We provide quantitative results for 20 objects. Tac2Pose provides high
accuracy pose estimations from distinctive tactile observations while
regressing meaningful pose distributions to account for those contact shapes
that could result from different object poses. We also test Tac2Pose on object
models reconstructed from a 3D scanner, to evaluate the robustness to
uncertainty in the object model. Finally, we demonstrate the advantages of
Tac2Pose compared with three baseline methods for tactile pose estimation:
directly regressing the object pose with a neural network, matching an observed
contact to a set of possible contacts using a standard classification neural
network, and direct pixel comparison of an observed contact with a set of
possible contacts.
  Website: http://mcube.mit.edu/research/tac2pose.html
