# Learning to Detect Slip through Tactile Estimation of the Contact Force Field and its Entropy

arXiv: https://arxiv.org/abs/2303.00935 · HTML: https://mlsanity.com/paper/2303.00935/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 25 references that are in the map.
- First posted on arXiv 2 Mar 2023.

## Builds on (most cited first, 6 of 6)

- [Cable Manipulation with a Tactile-Reactive Gripper](https://mlsanity.com/paper/1910.02860/) (2019)
- [Improved GelSight Tactile Sensor for Measuring Geometry and Slip](https://mlsanity.com/paper/1708.00922/) (2017)
- [Robotic Tactile Perception of Object Properties: A Review](https://mlsanity.com/paper/1711.03810/) (2017)
- [Slip Detection with Combined Tactile and Visual Information](https://mlsanity.com/paper/1802.10153/) (2018)
- [Maintaining Grasps within Slipping Bound by Monitoring Incipient Slip](https://mlsanity.com/paper/1810.13381/) (2018)
- [Leveraging distributed contact force measurements for slip detection: a physics-based approach enabled by a data-driven tactile sensor](https://mlsanity.com/paper/2109.11504/) (2021)

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

- [Blind Dexterous Grasping via Real2Sim2Real Tactile Policy Learning](https://mlsanity.com/paper/2606.11767/) (2026)
- [Investigating Sensors and Methods in Grasp State Classification in Agricultural Manipulation](https://mlsanity.com/paper/2508.11588/) (2025)
- [Symmetry-Aware Fusion of Vision and Tactile Sensing via Bilateral Force Priors for Robotic Manipulation](https://mlsanity.com/paper/2602.13689/) (2026)
- [SlipSense: Multimodal Tactile Learning for Low-Latency and Generalized Slip Detection](https://mlsanity.com/paper/2609.15910/) (2026)

## Abstract (arXiv)

Detection of slip during object grasping and manipulation plays a vital role in object handling. Existing solutions primarily rely on visual information to devise a strategy for grasping. However, for robotic systems to attain a level of proficiency comparable to humans, especially in consistently handling and manipulating unfamiliar objects, integrating artificial tactile sensing is increasingly essential. We introduce a novel physics-informed, data-driven approach to detect slip continuously in real time. We employ the GelSight Mini, an optical tactile sensor, attached to custom-designed grippers to gather tactile data. Our work leverages the inhomogeneity of tactile sensor readings during slip events to develop distinctive features and formulates slip detection as a classification problem. To evaluate our approach, we test multiple data-driven models on 10 common objects under different loading conditions, textures, and materials. Our results show that the best classification algorithm achieves a high average accuracy of 95.61%. We further illustrate the practical application of our research in dynamic robotic manipulation tasks, where our real-time slip detection and prevention algorithm is implemented.