# A model-free approach to fingertip slip and disturbance detection for grasp stability inference

arXiv: https://arxiv.org/abs/2311.13245 · HTML: https://mlsanity.com/paper/2311.13245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 37 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 8 of 8)

- [Data-Driven Grasp Synthesis - A Survey](https://mlsanity.com/paper/1309.2660/) (2013)
- [More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch](https://mlsanity.com/paper/1805.11085/) (2018)
- [ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact](https://mlsanity.com/paper/1904.03754/) (2019)
- [Slip Detection with Combined Tactile and Visual Information](https://mlsanity.com/paper/1802.10153/) (2018)
- [Leveraging Contact Forces for Learning to Grasp](https://mlsanity.com/paper/1809.07004/) (2018)
- [Neuromorphic Event-Based Slip Detection and suppression in Robotic Grasping and Manipulation](https://mlsanity.com/paper/2004.07386/) (2020)
- [Learning compliant grasping and manipulation by teleoperation with adaptive force control](https://mlsanity.com/paper/2107.08996/) (2021)
- [Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network](https://mlsanity.com/paper/2202.09549/) (2022)

## Abstract (arXiv)

Robotic capacities in object manipulation are incomparable to those of humans. Besides years of learning, humans rely heavily on the richness of information from physical interaction with the environment. In particular, tactile sensing is crucial in providing such rich feedback. Despite its potential contributions to robotic manipulation, tactile sensing is less exploited; mainly due to the complexity of the time series provided by tactile sensors. In this work, we propose a method for assessing grasp stability using tactile sensing. More specifically, we propose a methodology to extract task-relevant features and design efficient classifiers to detect object slippage with respect to individual fingertips. We compare two classification models: support vector machine and logistic regression. We use highly sensitive Uskin tactile sensors mounted on an Allegro hand to test and validate our method. Our results demonstrate that the proposed method is effective in slippage detection in an online fashion.