# Under Pressure: Learning to Detect Slip with Barometric Tactile Sensors

arXiv: https://arxiv.org/abs/2103.13460 · HTML: https://mlsanity.com/paper/2103.13460/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 42 references that are in the map.
- First posted on arXiv 24 Mar 2021.

## Builds on (most cited first, 13 of 13)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling](https://mlsanity.com/paper/1803.01271/) (2018)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [DIGIT: A Novel Design for a Low-Cost Compact High-Resolution Tactile Sensor with Application to In-Hand Manipulation](https://mlsanity.com/paper/2005.14679/) (2020)
- [Deep Drone Acrobatics](https://mlsanity.com/paper/2006.05768/) (2020)
- [GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger](https://mlsanity.com/paper/1803.00628/) (2018)
- [Object Handovers: a Review for Robotics](https://mlsanity.com/paper/2007.12952/) (2020)
- [SwingBot: Learning Physical Features from In-hand Tactile Exploration for Dynamic Swing-up Manipulation](https://mlsanity.com/paper/2101.11812/) (2021)
- [Maintaining Grasps within Slipping Bound by Monitoring Incipient Slip](https://mlsanity.com/paper/1810.13381/) (2018)
- [Towards Learning to Detect and Predict Contact Events on Vision-based Tactile Sensors](https://mlsanity.com/paper/1910.03973/) (2019)
- [Slip detection for grasp stabilisation with a multi-fingered tactile robot hand](https://mlsanity.com/paper/2010.01928/) (2020)

## Abstract (arXiv)

Despite the utility of tactile information, tactile sensors have yet to be widely deployed in industrial robotics settings. Part of the challenge lies in identifying slip and other key events from the tactile data stream. In this paper, we present a learning-based method to detect slip using barometric tactile sensors. Although these sensors have a low resolution, they have many other desirable properties including high reliability and durability, a very slim profile, and a low cost. We are able to achieve slip detection accuracies of greater than 91% while being robust to the speed and direction of the slip motion. Further, we test our detector on two robot manipulation tasks involving common household objects and demonstrate successful generalization to real-world scenarios not seen during training. We show that barometric tactile sensing technology, combined with data-driven learning, is potentially suitable for complex manipulation tasks such as slip compensation.