# Tactile Gesture Recognition with Built-in Joint Sensors for Industrial Robots

arXiv: https://arxiv.org/abs/2508.12435 · HTML: https://mlsanity.com/paper/2508.12435/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 33 references that are in the map.
- First posted on arXiv 17 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [Perceived Safety in Physical Human Robot Interaction -- A Survey](https://mlsanity.com/paper/2105.14499/) (2021)
- [Improving safety in physical human-robot collaboration via deep metric learning](https://mlsanity.com/paper/2302.11933/) (2023)
- [Towards Human Haptic Gesture Interpretation for Robotic Systems](https://mlsanity.com/paper/2012.01959/) (2020)

## Abstract (arXiv)

While gesture recognition using vision or robot skins is an active research area in Human-Robot Collaboration (HRC), this paper explores deep learning methods relying solely on a robot's built-in joint sensors, eliminating the need for external sensors. We evaluated various convolutional neural network (CNN) architectures and collected a dataset to study the impact of data representation and model architecture on the recognition accuracy. Our results show that spectrogram-based representations significantly improve accuracy, while model architecture plays a smaller role. We also tested generalization to new robot poses, where spectrogram-based models performed better. Implemented on a Franka Emika Research robot, two of our methods, STFT2DCNN and STT3DCNN, achieved over 95% accuracy in contact detection and gesture classification. These findings demonstrate the feasibility of external-sensor-free tactile recognition and promote further research toward cost-effective, scalable solutions for HRC.