# Tactile Estimation of Extrinsic Contact Patch for Stable Placement

arXiv: https://arxiv.org/abs/2309.14552 · HTML: https://mlsanity.com/paper/2309.14552/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 25 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Learning by Playing - Solving Sparse Reward Tasks from Scratch](https://mlsanity.com/paper/1802.10567/) (2018)
- [Reinforcement and Imitation Learning for Diverse Visuomotor Skills](https://mlsanity.com/paper/1802.09564/) (2018)
- [Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse Shapes](https://mlsanity.com/paper/2110.06192/) (2021)
- [Self-Supervised Sim-to-Real Adaptation for Visual Robotic Manipulation](https://mlsanity.com/paper/1910.09470/) (2019)
- [Active Extrinsic Contact Sensing: Application to General Peg-in-Hole Insertion](https://mlsanity.com/paper/2110.03555/) (2021)
- [Extrinsic Contact Sensing with Relative-Motion Tracking from Distributed Tactile Measurements](https://mlsanity.com/paper/2103.08108/) (2021)
- [Neural Contact Fields: Tracking Extrinsic Contact with Tactile Sensing](https://mlsanity.com/paper/2210.09297/) (2022)
- [Active Learning of Abstract Plan Feasibility](https://mlsanity.com/paper/2107.00683/) (2021)
- [Adaptive Curriculum Generation from Demonstrations for Sim-to-Real Visuomotor Control](https://mlsanity.com/paper/1910.07972/) (2019)
- [Simultaneous Tactile Estimation and Control of Extrinsic Contact](https://mlsanity.com/paper/2303.03385/) (2023)
- [Tactile-Filter: Interactive Tactile Perception for Part Mating](https://mlsanity.com/paper/2303.06034/) (2023)

## Built on by (most cited first, 1 of 1)

- [UNIC: Learning Unified Multimodal Extrinsic Contact Estimation](https://mlsanity.com/paper/2601.04356/) (2026)

## Abstract (arXiv)

Precise perception of contact interactions is essential for fine-grained manipulation skills for robots. In this paper, we present the design of feedback skills for robots that must learn to stack complex-shaped objects on top of each other (see Fig.1). To design such a system, a robot should be able to reason about the stability of placement from very gentle contact interactions. Our results demonstrate that it is possible to infer the stability of object placement based on tactile readings during contact formation between the object and its environment. In particular, we estimate the contact patch between a grasped object and its environment using force and tactile observations to estimate the stability of the object during a contact formation. The contact patch could be used to estimate the stability of the object upon release of the grasp. The proposed method is demonstrated in various pairs of objects that are used in a very popular board game.