PyTouch: A Machine Learning Library for Touch Processing
Research output: Contribution to book/Conference proceedings/Anthology/Report › Conference contribution › Contributed › peer-review
Contributors
Abstract
With the increased availability of rich tactile sensors, there is an an equally proportional need for open-source and integrated software capable of efficiently and effectively processing raw touch measurements into high-level signals that can be used for control and decision-making. In this paper, we present PyTouch – the first machine learning library dedicated to the processing of touch sensing signals. PyTouch, is designed to be modular, easy-to-use and provides state-of-the-art touch processing capabilities as a service with the goal of unifying the tactile sensing community by providing a library for building scalable, proven, and performance-validated modules over which applications and research can be built upon. We evaluate PyTouch on real-world data from several tactile sensors on touch processing tasks such as touch detection, slip and object pose estimations. PyTouch is open-sourced at https://github.com/facebookresearch/pytouch.
Details
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE International Conference on Robotics and Automation (ICRA) |
| Pages | 13208-13214 |
| Number of pages | 7 |
| ISBN (electronic) | 9781728190778 |
| Publication status | Published - 2021 |
| Peer-reviewed | Yes |
| Externally published | Yes |
External IDs
| ORCID | /0000-0001-9430-8433/work/146646297 |
|---|---|
| Scopus | 85125470260 |