  Robotic manipulation tasks such as object insertion typically involve
interactions between object and environment, namely extrinsic contacts. Prior
work on Neural Contact Fields (NCF) use intrinsic tactile sensing between
gripper and object to estimate extrinsic contacts in simulation. However, its
effectiveness and utility in real-world tasks remains unknown.
  In this work, we improve NCF to enable sim-to-real transfer and use it to
train policies for mug-in-cupholder and bowl-in-dishrack insertion tasks. We
find our model NCF-v2, is capable of estimating extrinsic contacts in the
real-world. Furthermore, our insertion policy with NCF-v2 outperforms policies
without it, achieving 33% higher success and 1.36x faster execution on
mug-in-cupholder, and 13% higher success and 1.27x faster execution on
bowl-in-dishrack.
