mlsanityData to 7 Oct 2026

Neural Contact Fields: Tracking Extrinsic Contact with Tactile Sensing

arXiv 2210.09297 ↗First posted 17 Oct 2022cs.RO cs.CVcited by 13

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Abstract (arXiv)

We present Neural Contact Fields, a method that brings together neural fields and tactile sensing to address the problem of tracking extrinsic contact between object and environment. Knowing where the external contact occurs is a first step towards methods that can actively control it in facilitating downstream manipulation tasks. Prior work for localizing environmental contacts typically assume a contact type (e.g. point or line), does not capture contact/no-contact transitions, and only works with basic geometric-shaped objects. Neural Contact Fields are the first method that can track arbitrary multi-modal extrinsic contacts without making any assumptions about the contact type. Our key insight is to estimate the probability of contact for any 3D point in the latent space of object shapes, given vision-based tactile inputs that sense the local motion resulting from the external contact. In experiments, we find that Neural Contact Fields are able to localize multiple contact patches without making any assumptions about the geometry of the contact, and capture contact/no-contact transitions for known categories of objects with unseen shapes in unseen environment configurations. In addition to Neural Contact Fields, we also release our YCB-Extrinsic-Contact dataset of simulated extrinsic contact interactions to enable further research in this area. Project page: https://github.com/carolinahiguera/NCF

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 29 of its 37 references that are in the map. First posted on arXiv 17 Oct 2022.

Builds on

29 of 37 references.

References · 29

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17 more
  1. 2020DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulationcited by 164
  2. 20213D Neural Scene Representations for Visuomotor Controlcited by 110
  3. 2022iSDF: Real-Time Neural Signed Distance Fields for Robot Perceptioncited by 107
  4. 2020TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensorscited by 59
  5. 2021Tactile-RL for Insertion: Generalization to Objects of Unknown Geometrycited by 45
  6. 2020Object Rearrangement Using Learned Implicit Collision Functionscited by 43
  7. 20203D Shape Reconstruction from Vision and Touchcited by 42
  8. 20183D Shape Perception from Monocular Vision, Touch, and Shape Priorscited by 31
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  10. 2020Tactile Object Pose Estimation from the First Touch with Geometric Contact Renderingcited by 19
  11. 2020Tactile SLAM: Real-time inference of shape and pose from planar pushingcited by 15
  12. 2022Tac2Pose: Tactile Object Pose Estimation from the First Touchcited by 14
  13. 2021Extrinsic Contact Sensing with Relative-Motion Tracking from Distributed Tactile Measurementscited by 13
  14. 2019Tactile-Based Insertion for Dense Box-Packingcited by 12
  15. 2021PatchGraph: In-hand tactile tracking with learned surface normalscited by 10
  16. 2021Full-Body Visual Self-Modeling of Robot Morphologiescited by 8
  17. 2019Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensorscited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 5; 2024: 5; 2025: 2; 2026: 1.

13 papers.

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