تقرير
Under Pressure: Learning to Detect Slip with Barometric Tactile Sensors
| العنوان: | Under Pressure: Learning to Detect Slip with Barometric Tactile Sensors |
|---|---|
| المؤلفون: | Grover, Abhinav, Grebe, Christopher, Nadeau, Philippe, Kelly, Jonathan |
| سنة النشر: | 2021 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Robotics, Computer Science - Artificial Intelligence |
| الوصف: | 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. Comment: In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'21) RoboTac Workshop: New Advances in Tactile Sensation, Interactive Perception, Control, and Learning, Prague, Czech Republic, Sep. 27, 2021 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2103.13460 |
| رقم الأكسشن: | edsarx.2103.13460 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |