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Perceiving Extrinsic Contacts from Touch Improves Learning Insertion Policies
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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.
Forward citations
Cited by 2 Pith papers
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NeuralTouch: Neural Descriptors for Precise Sim-to-Real Tactile Robot Control
A robot grasping system that uses neural descriptor fields to guide a tactile RL policy achieves sub-millimetre precision in simulation and zero-shot transfer to real peg-in-hole and bottle-lid-opening tasks.
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ViTaSCOPE: Visuo-tactile Implicit Representation for In-hand Pose and Extrinsic Contact Estimation
ViTaSCOPE jointly estimates in-hand object pose and extrinsic contact location on an object from vision and tactile shear fields, trained solely in simulation and deployed in the real world.
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