# Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network

arXiv: https://arxiv.org/abs/2202.09549 · HTML: https://mlsanity.com/paper/2202.09549/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 42 references that are in the map.
- First posted on arXiv 19 Feb 2022.

## Builds on (most cited first, 14 of 14)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [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)
- [Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks](https://mlsanity.com/paper/1602.07868/) (2016)
- [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)

## Built on by (most cited first, 1 of 1)

- [A model-free approach to fingertip slip and disturbance detection for grasp stability inference](https://mlsanity.com/paper/2311.13245/) (2023)

## Abstract (arXiv)

The ability to perceive object slip via tactile feedback enables humans to accomplish complex manipulation tasks including maintaining a stable grasp. Despite the utility of tactile information for many applications, tactile sensors have yet to be widely deployed in industrial robotics settings; part of the challenge lies in identifying slip and other events from the tactile data stream. In this paper, we present a learning-based method to detect slip using barometric tactile sensors. These sensors have many desirable properties including high durability and reliability, and are built from inexpensive, off-the-shelf components. We train a temporal convolution neural network to detect slip, achieving high detection accuracies while displaying robustness to the speed and direction of the slip motion. Further, we test our detector on two manipulation tasks involving a variety of common objects and demonstrate successful generalization to real-world scenarios not seen during training. We argue that barometric tactile sensing technology, combined with data-driven learning, is suitable for many manipulation tasks such as slip compensation.