# Learning Object Compliance via Young's Modulus from Single Grasps using Camera-Based Tactile Sensors

arXiv: https://arxiv.org/abs/2406.15304 · HTML: https://mlsanity.com/paper/2406.15304/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2024.

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

- [Advances in Compliance Detection: Novel Models Using Vision-Based Tactile Sensors](https://mlsanity.com/paper/2506.14980/) (2025)

## Abstract (arXiv)

Compliance is a useful parametrization of tactile information that humans often utilize in manipulation tasks. It can be used to inform low-level contact-rich actions or characterize objects at a high-level. In robotic manipulation, existing approaches to estimate compliance have struggled to generalize across both object shape and material. Using camera-based tactile sensors, proprioception, and force measurements, we present a novel approach to estimate object compliance as Young's modulus (E) from parallel grasps. We evaluate our method over a novel dataset of 285 common objects, including a wide array of shapes and materials with Young's moduli ranging from 5.0 kPa to 250 GPa. Combining analytical and data-driven approaches, we develop a hybrid system using a multi-tower neural network to analyze a sequence of tactile images from grasping. This system is shown to estimate the Young's modulus of unseen objects within an order of magnitude at 74.2% accuracy across our dataset. This is an improvement over purely analytical and data-driven baselines which exhibit 28.9% and 65.0% accuracy respectively. Importantly, this estimation system performs irrespective of object geometry and demonstrates increased robustness across material types. Code is available on GitHub and collected data is available on HuggingFace.