# Slip Detection with Combined Tactile and Visual Information

arXiv: https://arxiv.org/abs/1802.10153 · HTML: https://mlsanity.com/paper/1802.10153/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2018.

## Built on by (most cited first, 23 of 23)

- [Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks](https://mlsanity.com/paper/2406.13640/) (2024)
- [OmniVTLA: Vision-Tactile-Language-Action Models with Semantic-Aligned Tactile Sensing](https://mlsanity.com/paper/2508.08706/) (2025)
- [AnySkin: Plug-and-play Skin Sensing for Robotic Touch](https://mlsanity.com/paper/2409.08276/) (2024)
- [Visuo-Haptic Object Perception for Robots: An Overview](https://mlsanity.com/paper/2203.11544/) (2022)
- [FingerSLAM: Closed-loop Unknown Object Localization and Reconstruction from Visuo-tactile Feedback](https://mlsanity.com/paper/2303.07997/) (2023)
- [Evetac: An Event-based Optical Tactile Sensor for Robotic Manipulation](https://mlsanity.com/paper/2312.01236/) (2023)
- [Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins](https://mlsanity.com/paper/2410.17246/) (2024)
- [eFlesh: Highly customizable Magnetic Touch Sensing using Cut-Cell Microstructures](https://mlsanity.com/paper/2506.09994/) (2025)
- [Learning to Detect Slip through Tactile Estimation of the Contact Force Field and its Entropy](https://mlsanity.com/paper/2303.00935/) (2023)
- [Learning Force Distribution Estimation for the GelSight Mini Optical Tactile Sensor Based on Finite Element Analysis](https://mlsanity.com/paper/2411.03315/) (2024)
- [PyTouch: A Machine Learning Library for Touch Processing](https://mlsanity.com/paper/2105.12791/) (2021)
- [PoseIt: A Visual-Tactile Dataset of Holding Poses for Grasp Stability Analysis](https://mlsanity.com/paper/2209.05022/) (2022)
- [Multimodal perception for dexterous manipulation](https://mlsanity.com/paper/2112.14298/) (2021)
- [Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots](https://mlsanity.com/paper/2403.03563/) (2024)
- [Investigating Sensors and Methods in Grasp State Classification in Agricultural Manipulation](https://mlsanity.com/paper/2508.11588/) (2025)
- [Tactile-ViewGCN: Learning Shape Descriptor from Tactile Data using Graph Convolutional Network](https://mlsanity.com/paper/2203.06183/) (2022)
- [Towards Design and Development of an ArUco Markers-Based Quantitative Surface Tactile Sensor](https://mlsanity.com/paper/2310.08398/) (2023)
- [RoboSense At Edge: Detecting Slip, Crumple and Shape of the Object in Robotic Hand for Teleoprations](https://mlsanity.com/paper/2311.07888/) (2023)
- [A model-free approach to fingertip slip and disturbance detection for grasp stability inference](https://mlsanity.com/paper/2311.13245/) (2023)
- [ControlTac: Scaling Tactile Data with Physically Controlled Tactile Image Generation](https://mlsanity.com/paper/2505.20498/) (2025)
- [A Physical Agentic Loop for Language-Guided Grasping with Execution-State Monitoring](https://mlsanity.com/paper/2604.07395/) (2026)
- [More with LESS -- Local Scene Representations for Tactile Imaging](https://mlsanity.com/paper/2606.14344/) (2026)
- [CARE: Experience-Guided Atomic Corrective Execution for Vision-Language-Action Policies](https://mlsanity.com/paper/2609.24118/) (2026)

## Abstract (arXiv)

Slip detection plays a vital role in robotic manipulation and it has long been a challenging problem in the robotic community. In this paper, we propose a new method based on deep neural network (DNN) to detect slip. The training data is acquired by a GelSight tactile sensor and a camera mounted on a gripper when we use a robot arm to grasp and lift 94 daily objects with different grasping forces and grasping positions. The DNN is trained to classify whether a slip occurred or not. To evaluate the performance of the DNN, we test 10 unseen objects in 152 grasps. A detection accuracy as high as 88.03% is achieved. It is anticipated that the accuracy can be further improved with a larger dataset. This method is beneficial for robots to make stable grasps, which can be widely applied to automatic force control, grasping strategy selection and fine manipulation.