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Symmetry
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Symmetry and Applications in Cognitive Robotics
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Symmetry and Applications in Cognitive Robotics

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 10247

Special Issue Editors

Prof. Dr. Zhixun Su

E-Mail Website
Guest Editor
School of Mathematical Sciences, Dalian University of Technology, Dalian, China
Interests: machine learning; computer vision; computational geometry
Dr. Rushi Lan

E-Mail Website
Guest Editor
School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, China
Interests: image processing; machine learning

Special Issue Information

Dear Colleagues,

Cognitive robotics (CR) endows a robot with intelligent behavior that merges the physical systems and controls the different fields of architecture. The first field is to adapt to dynamic environments, while the latter explicitly considers the need to acquire and exploit past experiences. Recently, the study of CR has become interdisciplinary, inspired by knowledge and methods from many different areas, such as psychology, biology, signal processing, physics, information theory, mathematics, and mechanical science. The designed cognitive robots will achieve their goals via understanding the semantic meaning of unknown environments, intelligent sensing, automatic planning, multi-agent communication, and anticipation of the outcome of actions. Symmetry theory plays an important role in CR. For example, it can identify invariant sensor–actuator signal relations, robot cognition using Bayesian symmetry networks, and it can also be used in cognitive symmetry engines in the cognitive robotics field.

The development of CR will keep cross-fertilizing these research areas. This Special Issue aims to promote technologies such as artificial intelligence, scientific computing, and computational neuroscience. Those areas are the best tools for upgrading robots with near-human intelligence. The overall goal of this Special Issue is to foster links between the fields of cognitive science, robotics, and machine intelligence and promote the methodologies required to for industrial and social research on CR.

Specifically, the aim of this Special Issue is to collect state-of-the-art contributions on deep neural networks, image processing, computational cognition and perception, machine vision, natural language processing, human action analysis, digital home, medical information processing, and related applications in robotics.

Please note that all submitted papers must be within the general scope of the Symmetry journal.

Prof. Dr. Zhixun Su
Dr. Rushi Lan
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Symmetry is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cognitive computing
  • machine learning for robots
  • computer vision for robot applications
  • robotic sensor networks
  • behavioral analysis of robots
  • digital home techniques and applications

Published Papers (5 papers)

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Research

14 pages, 4863 KiB  
Article
Mesh Denoising Based on Recurrent Neural Networks
by Yan Xing, Jieqing Tan, Peilin Hong, Yeyuan He and Min Hu
Symmetry 2022, 14(6), 1233; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14061233 - 14 Jun 2022
Cited by 4 | Viewed by 1512
Abstract
Mesh denoising is a classical task in mesh processing. Many state-of-the-art methods are still unable to quickly and robustly denoise multifarious noisy 3D meshes, especially in the case of high noise. Recently, neural network-based models have played a leading role in natural language, [...] Read more.
Mesh denoising is a classical task in mesh processing. Many state-of-the-art methods are still unable to quickly and robustly denoise multifarious noisy 3D meshes, especially in the case of high noise. Recently, neural network-based models have played a leading role in natural language, audio, image, video, and 3D model processing. Inspired by these works, we propose a data-driven mesh denoising method based on recurrent neural networks, which learns the relationship between the feature descriptors and the ground-truth normals. The recurrent neural network has a feedback loop before entering the output layer. By means of the self-feedback of neurons, the output of a recurrent neural network is related not only to the current input but also to the output of the previous moments. To deal with meshes with various geometric features, we use k-means to cluster the faces of the mesh according to geometric similarity and train neural networks for each category individually in the offline learning stage. Each network model, acting similar to a normal regression function, will map the geometric feature descriptor of each facet extracted from the mesh to the denoised facet normal. Then, the denoised normals are used to calculate the new feature descriptors, which become the input of the next similar regression model. In this system, three normal regression modules are cascaded to generate the last facet normals. Lastly, the model’s vertex positions are updated according to the denoised normals. A large number of visual and numerical results have demonstrated that the proposed model outperforms the state-of-the-art methods in most cases. Full article
(This article belongs to the Special Issue Symmetry and Applications in Cognitive Robotics)
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20 pages, 2200 KiB  
Article
Temporally Multi-Modal Semantic Reasoning with Spatial Language Constraints for Video Question Answering
by Mingyang Liu, Ruomei Wang, Fan Zhou and Ge Lin
Symmetry 2022, 14(6), 1133; https://doi.org/10.3390/sym14061133 - 31 May 2022
Cited by 1 | Viewed by 1435
Abstract
Video question answering (QA) aims to understand the video scene and underlying plot by answering video questions. An algorithm that can competently cope with this task needs to be able to: (1) collect multi-modal information scattered in the video frame sequence while extracting, [...] Read more.
Video question answering (QA) aims to understand the video scene and underlying plot by answering video questions. An algorithm that can competently cope with this task needs to be able to: (1) collect multi-modal information scattered in the video frame sequence while extracting, interpreting, and utilizing the potential semantic clues provided by each piece of modal information in the video, (2) integrate the multi-modal context of the above semantic clues and understand the cause and effect of the story as it evolves, and (3) identify and integrate those temporally adjacent or non-adjacent effective semantic clues implied in the above context information to provide reasonable and sufficient visual semantic information for the final question reasoning. In response to the above requirements, a novel temporally multi-modal semantic reasoning with spatial language constraints video QA solution is reported in this paper, which includes a significant feature extraction module used to extract multi-modal features according to a significant sampling strategy, a spatial language constraints module used to recognize and reason spatial dimensions in video frames under the guidance of questions, and a temporal language interaction module used to locate the temporal dimension semantic clues of the appearance features and motion features sequence. Specifically, for a question, the result processed by the spatial language constraints module is to obtain visual clues related to the question from a single image and filter out unwanted spatial information. Further, the temporal language interaction module symmetrically integrates visual clues of the appearance information and motion information scattered throughout the temporal dimensions, obtains the temporally adjacent or non-adjacent effective semantic clue, and filters out irrelevant or detrimental context information. The proposed video QA solution is validated on several video QA benchmarks. Comprehensive ablation experiments have confirmed that modeling the significant video information can improve QA ability. The spatial language constraints module and temporal language interaction module can better collect and summarize visual semantic clues. Full article
(This article belongs to the Special Issue Symmetry and Applications in Cognitive Robotics)
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14 pages, 4092 KiB  
Article
TACDFSL: Task Adaptive Cross Domain Few-Shot Learning
by Qi Zhang, Yingluo Jiang and Zhijie Wen
Symmetry 2022, 14(6), 1097; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14061097 - 27 May 2022
Cited by 1 | Viewed by 1831
Abstract
Cross Domain Few-Shot Learning (CDFSL) has attracted the attention of many scholars since it is closer to reality. The domain shift between the source domain and the target domain is a crucial problem for CDFSL. The essence of domain shift is the marginal [...] Read more.
Cross Domain Few-Shot Learning (CDFSL) has attracted the attention of many scholars since it is closer to reality. The domain shift between the source domain and the target domain is a crucial problem for CDFSL. The essence of domain shift is the marginal distribution difference between two domains which is implicit and unknown. So the empirical marginal distribution measurement is proposed, that is, WDMDS (Wasserstein Distance for Measuring Domain Shift) and MMDMDS (Maximum Mean Discrepancy for Measuring Domain Shift). Besides this, pre-training a feature extractor and fine-tuning a classifier are used in order to have a good generalization in CDFSL. Since the feature obtained by the feature extractor is high-dimensional and left-biased, the adaptive feature distribution transformation is proposed, to make the feature distribution of each sample be approximately Gaussian distribution. This approximate symmetric distribution improves image classification accuracy by 3% on average. In addition, the applicability of different classifiers for CDFSL is investigated, and the classification model should be selected based on the empirical marginal distribution difference between the two domains. The Task Adaptive Cross Domain Few-Shot Learning (TACDFSL) is proposed based on the above ideas. TACDFSL improves image classification accuracy by 3–9%. Full article
(This article belongs to the Special Issue Symmetry and Applications in Cognitive Robotics)
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18 pages, 3191 KiB  
Article
SNPD: Semi-Supervised Neural Process Dehazing Network with Asymmetry Pseudo Labels
by Fan Zhou, Xiaozhe Meng, Yuxin Feng and Zhuo Su
Symmetry 2022, 14(4), 806; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14040806 - 13 Apr 2022
Cited by 3 | Viewed by 1459
Abstract
Haze can cause a significant reduction in the contrast and brightness of images. CNN-based methods have achieved benign performance on synthetic data. However, they show weak generalization performance on real data because they are only trained on fully labeled data, ignoring the role [...] Read more.
Haze can cause a significant reduction in the contrast and brightness of images. CNN-based methods have achieved benign performance on synthetic data. However, they show weak generalization performance on real data because they are only trained on fully labeled data, ignoring the role of natural data in the network. That is, there exists distribution shift. In addition to using little real data for training image dehazing networks in the literature, few studies have designed losses to constrain the intermediate latent space and the output simultaneously. This paper presents a semi-supervised neural process dehazing network with asymmetry pseudo labels. First, we use labeled data to train a backbone network and save intermediate latent features and parameters. Then, in the latent space, the neural process maps the latent features of real data to the latent space of synthetic data to generate one pseudo label. One neural process loss is proposed here. For situations where the image may be darker after dehazing, another pseudo label is created, and one new loss is used to guide the dehazing result at the output end. We combine the two pseudo labels with designed losses to suppress the distribution shift and guide better dehazing results. Finally, the artificial and hazy natural images are tested experimentally to demonstrate the method’s effectiveness. Full article
(This article belongs to the Special Issue Symmetry and Applications in Cognitive Robotics)
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18 pages, 3998 KiB  
Article
Asymmetric Identification Model for Human-Robot Contacts via Supervised Learning
by Qasem Abu Al-Haija and Ja’afer Al-Saraireh
Symmetry 2022, 14(3), 591; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14030591 - 16 Mar 2022
Cited by 19 | Viewed by 3008
Abstract
Human-robot interaction (HRI) occupies an essential role in the flourishing market for intelligent robots for a wide range of asymmetric personal and entertainment applications, ranging from assisting older people and the severely disabled to the entertainment robots at amusement parks. Improving the way [...] Read more.
Human-robot interaction (HRI) occupies an essential role in the flourishing market for intelligent robots for a wide range of asymmetric personal and entertainment applications, ranging from assisting older people and the severely disabled to the entertainment robots at amusement parks. Improving the way humans and machines interact can help democratize robotics. With machine and deep learning techniques, robots will more easily adapt to new tasks, conditions, and environments. In this paper, we develop, implement, and evaluate the performance of the machine-learning-based HRI model in a collaborative environment. Specifically, we examine five supervised machine learning models viz. the ensemble of bagging trees (EBT) model, the k-nearest neighbor (kNN) model, the logistic regression kernel (LRK), the fine decision trees (FDT), and the subspace discriminator (SDC). The proposed models have been evaluated on an ample and modern contact detection dataset (CDD 2021). CDD 2021 is gathered from a real-world robot arm, Franka Emika Panda, when it was executing repetitive asymmetric movements. Typical performance assessment factors are applied to assess the model effectiveness in terms of detection accuracy, sensitivity, specificity, speed, and error ratios. Our experiential evaluation shows that the ensemble technique provides higher performance with a lower error ratio compared with other developed supervised models. Therefore, this paper proposes an ensemble-based bagged trees (EBT) detection model for classifying physical human–robot contact into three asymmetric types of contacts, including noncontact, incidental, and intentional. Our experimental results exhibit outstanding contact detection performance metrics scoring 97.1%, 96.9%, and 97.1% for detection accuracy, precision, and sensitivity, respectively. Besides, a low prediction overhead has been observed for the contact detection model, requiring a 102 µS to provide the correct detection state. Hence, the developed scheme can be efficiently adopted through the application requiring physical human–robot contact to give fast accurate detection to the contacts between the human arm and the robot arm. Full article
(This article belongs to the Special Issue Symmetry and Applications in Cognitive Robotics)
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