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Robotic manipulation tasks such as object insertion typically involveinteractions between object and environment, namely extrinsic contacts. Priorwork on Neural Contact Fields (NCF) use intrinsic tactile sensing betweengripper and object to estimate extrinsic contacts in simulation. However, itseffectiveness and utility in real-world tasks remains unknown.  In this work, we improve NCF to enable sim-to-real transfer and use it totrain policies for mug-in-cupholder and bowl-in-dishrack insertion tasks. Wefind our model NCF-v2, is capable of estimating extrinsic contacts in thereal-world. Furthermore, our insertion policy with NCF-v2 outperforms policieswithout it, achieving 33% higher success and 1.36x faster execution onmug-in-cupholder, and 13% higher success and 1.27x faster execution onbowl-in-dishrack.

Perceiving Extrinsic Contacts from Touch Improves Learning Insertion Policies