# Extrinsic Contact Sensing with Relative-Motion Tracking from Distributed Tactile Measurements

arXiv: https://arxiv.org/abs/2103.08108 · HTML: https://mlsanity.com/paper/2103.08108/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2021.

## Built on by (most cited first, 13 of 13)

- [Learning to Read Braille: Bridging the Tactile Reality Gap with Diffusion Models](https://mlsanity.com/paper/2304.01182/) (2023)
- [A Survey of Robotic Navigation and Manipulation with Physics Simulators in the Era of Embodied AI](https://mlsanity.com/paper/2505.01458/) (2025)
- [Neural Contact Fields: Tracking Extrinsic Contact with Tactile Sensing](https://mlsanity.com/paper/2210.09297/) (2022)
- [Tactile-Filter: Interactive Tactile Perception for Part Mating](https://mlsanity.com/paper/2303.06034/) (2023)
- [Learning to Synthesize Volumetric Meshes from Vision-based Tactile Imprints](https://mlsanity.com/paper/2203.15155/) (2022)
- [Integrated Object Deformation and Contact Patch Estimation from Visuo-Tactile Feedback](https://mlsanity.com/paper/2305.14470/) (2023)
- [ViTaSCOPE: Visuo-tactile Implicit Representation for In-hand Pose and Extrinsic Contact Estimation](https://mlsanity.com/paper/2506.12239/) (2025)
- [All You Need is LUV: Unsupervised Collection of Labeled Images using Invisible UV Fluorescent Indicators](https://mlsanity.com/paper/2203.04566/) (2022)
- [Tactile Estimation of Extrinsic Contact Patch for Stable Placement](https://mlsanity.com/paper/2309.14552/) (2023)
- [PolyFit: A Peg-in-hole Assembly Framework for Unseen Polygon Shapes via Sim-to-real Adaptation](https://mlsanity.com/paper/2312.02531/) (2023)
- [Semantic-Geometric-Physical-Driven Robot Manipulation Skill Transfer via Skill Library and Tactile Representation](https://mlsanity.com/paper/2411.11714/) (2024)
- [TranTac: Leveraging Transient Tactile Signals for Contact-Rich Robotic Manipulation](https://mlsanity.com/paper/2509.16550/) (2025)
- [UNIC: Learning Unified Multimodal Extrinsic Contact Estimation](https://mlsanity.com/paper/2601.04356/) (2026)

## Abstract (arXiv)

This paper addresses the localization of contacts of an unknown grasped rigid object with its environment, i.e., extrinsic to the robot. We explore the key role that distributed tactile sensing plays in localizing contacts external to the robot, in contrast to the role that aggregated force/torque measurements play in localizing contacts on the robot. When in contact with the environment, an object will move in accordance with the kinematic and possibly frictional constraints imposed by that contact. Small motions of the object, which are observable with tactile sensors, indirectly encode those constraints and the geometry that defines them. We formulate the extrinsic contact sensing problem as a constraint-based estimation. The estimation is subject to the kinematic constraints imposed by the tactile measurements of object motion, as well as the kinematic (e.g., non-penetration) and possibly frictional (e.g., sticking) constraints imposed by rigid-body mechanics. We validate the approach in simulation and with real experiments on the case studies of fixed point and line contacts. This paper discusses the theoretical basis for the value of distributed tactile sensing in contrast to aggregated force/torque measurements. It also provides an estimation framework for localizing environmental contacts with potential impact in contact-rich manipulation scenarios such as assembling or packing.