End-to-End ConvNet for Tactile Recognition Using Residual Orthogonal Tiling and Pyramid Convolution Ensemble (original) (raw)
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Proceedings of the AAAI Conference on Artificial Intelligence
Robotic tactile recognition aims at identifying target objects or environments from tactile sensory readings. The advancement of unsupervised feature learning and biological tactile sensing inspire us proposing the model of 3T-RTCN that performs spatio-temporal feature representation and fusion for tactile recognition. It decomposes tactile data into spatial and temporal threads, and incorporates the strength of randomized tiling convolutional networks. Experimental evaluations show that it outperforms some state-of-the-art methods with a large margin regarding recognition accuracy, robustness, and fault-tolerance; we also achieve an order-of-magnitude speedup over equivalent networks with pretraining and finetuning. Practical suggestions and hints are summarized in the end for effectively handling the tactile data.
Using 3D Convolutional Neural Networks for Tactile Object Recognition with Robotic Palpation
Sensors
In this paper, a novel method of active tactile perception based on 3D neural networks and a high-resolution tactile sensor installed on a robot gripper is presented. A haptic exploratory procedure based on robotic palpation is performed to get pressure images at different grasping forces that provide information not only about the external shape of the object, but also about its internal features. The gripper consists of two underactuated fingers with a tactile sensor array in the thumb. A new representation of tactile information as 3D tactile tensors is described. During a squeeze-and-release process, the pressure images read from the tactile sensor are concatenated forming a tensor that contains information about the variation of pressure matrices along with the grasping forces. These tensors are used to feed a 3D Convolutional Neural Network (3D CNN) called 3D TactNet, which is able to classify the grasped object through active interaction. Results show that 3D CNN performs bet...
Smart Tactile Sensing Systems Based on Embedded CNN Implementations
Micromachines
Embedding machine learning methods into the data decoding units may enable the extraction of complex information making the tactile sensing systems intelligent. This paper presents and compares the implementations of a convolutional neural network model for tactile data decoding on various hardware platforms. Experimental results show comparable classification accuracy of 90.88% for Model 3, overcoming similar state-of-the-art solutions in terms of time inference. The proposed implementation achieves a time inference of 1.2 ms while consuming around 900 μ J. Such an embedded implementation of intelligent tactile data decoding algorithms enables tactile sensing systems in different application domains such as robotics and prosthetic devices.
An Application of Deep Learning to Tactile Data for Object Recognition under Visual Guidance
Sensors
Drawing inspiration from haptic exploration of objects by humans, the current work proposes a novel framework for robotic tactile object recognition, where visual information in the form of a set of visually interesting points is employed to guide the process of tactile data acquisition. Neuroscience research confirms the integration of cutaneous data as a response to surface changes sensed by humans with data from joints, muscles, and bones (kinesthetic cues) for object recognition. On the other hand, psychological studies demonstrate that humans tend to follow object contours to perceive their global shape, which leads to object recognition. In compliance with these findings, a series of contours are determined around a set of 24 virtual objects from which bimodal tactile data (kinesthetic and cutaneous) are obtained sequentially and by adaptively changing the size of the sensor surface according to the object geometry for each object. A virtual Force Sensing Resistor array (FSR) ...
Sensors
Transfer of learning or leveraging a pre-trained network and fine-tuning it to perform new tasks has been successfully applied in a variety of machine intelligence fields, including computer vision, natural language processing and audio/speech recognition. Drawing inspiration from neuroscience research that suggests that both visual and tactile stimuli rouse similar neural networks in the human brain, in this work, we explore the idea of transferring learning from vision to touch in the context of 3D object recognition. In particular, deep convolutional neural networks (CNN) pre-trained on visual images are adapted and evaluated for the classification of tactile data sets. To do so, we ran experiments with five different pre-trained CNN architectures and on five different datasets acquired with different technologies of tactile sensors including BathTip, Gelsight, force-sensing resistor (FSR) array, a high-resolution virtual FSR sensor, and tactile sensors on the Barrett robotic han...
Tactile Convolutional Networks for Online Slip and Rotation Detection
Lecture Notes in Computer Science, 2016
We present a deep convolutional neural network which is capable to distinguish between different contact states in robotic manipulation tasks. By integrating spatial and temporal tactile sensor data from a piezo-resistive sensor array through deep learning techniques, the network is not only able to classify the contact state into stable versus slipping, but also to distinguish between rotational and translation slippage. We evaluated different network layouts and reached a final classification rate of more than 97%. Using consumer class GPUs, slippage and rotation events can be detected within 10 ms, which is still feasible for adaptive grasp control.
Tactile-ViewGCN: Learning Shape Descriptor from Tactile Data using Graph Convolutional Network
arXiv:2203.06183, 2022
For humans, our "senses of touch" have always been necessary for our ability to precisely and efficiently manipulate objects of all shapes in any environment, but until recently, not many works have been done to fully understand haptic feedback. This work proposed a novel method for getting a better shape descriptor than existing methods for classifying an object from multiple tactile data collected from a tactile glove. It focuses on improving previous works on object classification using tactile data. The major problem for object classification from multiple tactile data is to find a good way to aggregate features extracted from multiple tactile images. We propose a novel method, dubbed as Tactile-ViewGCN, that hierarchically aggregate tactile features considering relations among different features by using Graph Convolutional Network. Our model outperforms previous methods on the STAG dataset with an accuracy of 81.82%.
IEEE Transactions on Haptics, 2020
Humans can perceive tactile directionality with angular perception thresholds of 14-40 via fingerpad skin displacement. Using deformable, artificial tactile sensors, the ability to perceive tactile directionality was developed for a robotic system to aid in object manipulation tasks. Two convolutional neural networks (CNNs) were trained on tactile images created from fingerpad deformation measurements during perturbations to a handheld object. A primary CNN regression model provided a point estimate of tactile directionality over a range of grip forces, perturbation angles, and perturbation speeds. A secondary CNN model provided a variance estimate that was used to determine uncertainty about the point estimate. A 5-fold cross-validation was performed to evaluate model performance. The primary CNN produced tactile directionality point estimates with an error rate of 4.3% for a 20 angular resolution and was benchmarked against an open-source force estimation network. The model was implemented in real-time for interactions with an external agent and the environment with different object shapes and widths. The perception of tactile directionality could be used to enhance the situational awareness of human operators of telerobotic systems and to develop decision-making algorithms for context-appropriate responses by semi-autonomous robots.