This is our first work on grip detection for the COMMANDIA project using low-cost visual-based tactile sensors known as DIGIT.
Robotic manipulation continues being an unsolved problem. It involves many complex aspects, for example, perception tactile of different objects and materials, grasping control to plan the robotic hand pose, etc. Most of previous works on this topic used expensive sensors. This fact makes difficult the application in the industry. In this work, we propose a grip detection system using a low-cost visual-based tactile sensor known as DIGIT, mounted on a ROBOTIQ gripper 2F-140. We proved that a Deep Convolutional Network is able to detect contact or no contact. Capturing almost 16500 images with contact and no contact from different objects, we achieve 99% accuracy with never seen samples, in the best scenario. As a result, this system will allow us to implement a grasping controller for the gripper.
Castaño-Amoros, J.; Gil, P. and Puente, S. (2021). Touch Detection with Low-cost Visual-based Sensor. In Proceedings of the 2nd International Conference on Robotics, Computer Vision and Intelligent Systems, ISBN 978-989-758-537-1, pages 136-142.
