  Detecting and localizing contacts is essential for robot manipulators to
perform contact-rich tasks in unstructured environments. While robot skins can
localize contacts on the surface of robot arms, these sensors are not yet
robust or easily accessible. As such, prior works have explored using
proprioceptive observations, such as joint velocities and torques, to perform
contact localization. Many past approaches assume the robot is static during
contact incident, a single contact is made at a time, or having access to
accurate dynamics models and joint torque sensing. In this work, we relax these
assumptions and propose using Domain Randomization to train a neural network to
localize contacts of robot arms in motion without joint torque observations.
Our method uses a novel cylindrical projection encoding of the robot arm
surface, which allows the network to use convolution layers to process input
features and transposed convolution layers to predict contacts. The trained
network achieves a contact detection accuracy of 91.5% and a mean contact
localization error of 3.0cm. We further demonstrate an application of the
contact localization model in an obstacle mapping task, evaluated in both
simulation and the real world.
