Grasping Force Estimation for Markerless Visuotactile Sensors

Tactile sensors have been used for force estimation in the past, especially Vision-Based Tactile Sensors (VBTS) have recently become a new trend due to their high spatial resolution and low cost. In this work, we have designed and implemented several approaches to estimate the normal grasping force using different types of markerless visuotactile representations obtained from VBTS. Our main goal is to determine the most appropriate visuotactile representation, based on a performance analysis during robotic grasping tasks. Our proposal has been tested on the dataset generated with our DIGIT sensors and another one obtained using GelSight Mini sensors from another state-of-the-art work. We have also tested the generalization capabilities of our best approach, called RGBmod. The results led to two main conclusions. First, the RGB visuotactile representation is a better input option than the depth image or a combination of the two for estimating normal grasping forces. Second, RGBmod achieved a good performance when tested on 10 unseen everyday objects in real-world scenarios, achieving an average relative error of 0.125 +- 0.153. Furthermore, we show that our proposal outperforms other works in the literature that use RGB and depth information for the same task.

Rotational Slippage Prediction from Segmentation of Tactile Images

Adding tactile sensors to a robotic system is becoming a common practice to achieve more complex manipulation skills than those robotics systems that only use external cameras to manipulate objects. The key of tactile sensors is that they provide extra information about the physical properties of the grasping. In this paper, we implemented a system to predict and quantify the rotational slippage of objects in hand using the vision-based tactile sensor known as Digit. Our system comprises a neural network that obtains the segmented contact region (object-sensor), to later calculate the slippage rotation angle from this region using a thinning algorithm. Besides, we created our own tactile segmentation dataset, which is the first one in the literature as far as we are concerned, to train and evaluate our neural network, obtaining results of 95% and 91% in Dice and IoU metrics. In real-scenario experiments, our system is able to predict rotational slippage with a maximum mean rotational error of 3 degrees with previously unseen objects. Thus, our system can be used to prevent an object from falling due to its slippage.