# Slip detection for grasp stabilisation with a multi-fingered tactile robot hand

arXiv: https://arxiv.org/abs/2010.01928 · HTML: https://mlsanity.com/paper/2010.01928/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [FeelAnyForce: Estimating Contact Force Feedback from Tactile Sensation for Vision-Based Tactile Sensors](https://mlsanity.com/paper/2410.02048/) (2024)
- [GelFlow: Self-supervised Learning of Optical Flow for Vision-Based Tactile Sensor Displacement Measurement](https://mlsanity.com/paper/2309.06735/) (2023)
- [Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network](https://mlsanity.com/paper/2202.09549/) (2022)
- [Under Pressure: Learning to Detect Slip with Barometric Tactile Sensors](https://mlsanity.com/paper/2103.13460/) (2021)

## Abstract (arXiv)

Tactile sensing is used by humans when grasping to prevent us dropping objects. One key facet of tactile sensing is slip detection, which allows a gripper to know when a grasp is failing and take action to prevent an object being dropped. This study demonstrates the slip detection capabilities of the recently developed Tactile Model O (T-MO) by using support vector machines to detect slip and test multiple slip scenarios including responding to the onset of slip in real time with eleven different objects in various grasps. We demonstrate the benefits of slip detection in grasping by testing two real-world scenarios: adding weight to destabilise a grasp and using slip detection to lift up objects at the first attempt. The T-MO is able to detect when an object is slipping, react to stabilise the grasp and be deployed in real-world scenarios. This shows the T-MO is a suitable platform for autonomous grasping by using reliable slip detection to ensure a stable grasp in unstructured environments. Supplementary video: https://youtu.be/wOwFHaiHuKY