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SSRN 2024年

作者： Yang, Junpeng Li, Qiufu Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen University Guangdong Shenzhen518172 China

3D face recognition performs better than 2D face recognition, in terms of robustness against lighting and digital attacks. Compared to 2D data, the rich geometric information in 3D data could be very useful to improve the performance of 3D face recognition. This paper introduces PointSurFace, a surface feature extraction integrated model tailored for point cloud based 3D face recognition. PointSurFace adopts a specially designed surface feature extraction module, which explores information about the position and normals of the 3D face surface to explicitly extract local discriminative features from 3D point cloud faces. Subsequently, it alternates between set abstraction (SA) layers and Inverted Residual MLP (InvResMLP) modules to effectively extract features from the point cloud data. To better handle the spatial relationships in the point cloud data, PointSurFace integrates coordinate and feature data within each SA layer and InvResMLP module. It also applies channel fusion operations to align these different types of data, thereby more effectively extracts point cloud features. Experimental results in 3D facial recognition and verification across multiple datasets demonstrate that PointSurFace achieves the best 3D face recognition performance to date. For example, on Lock3DFace, it achieves the state-of-the-art recognition and verification accuracy of 90.03% and 80.01%, respectively, surpassing previous methods by 2.02% and 3.19%. Results on cross-quality datasets also affirm PointSurFace’s strong generalization capabilities. Ablation experiments validate the effectiveness of each module within PointSurFace, which suggests that the proposed surface feature extraction module significantly enhances the point cloud based 3D facial recognition performance. © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Learning a Better Control Barrier Function

arXiv

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arXiv 2022年

作者： Dai, Bolun Krishnamurthy, Prashanth Khorrami, Farshad Control/Robotics Research Laboratory Electrical & Computer Engineering Department Tandon School of Engineering New York University BrooklynNY11201 United States

Control barrier functions (CBFs) are widely used in safety-critical controllers. However, constructing a valid CBF is challenging, especially under nonlinear or non-convex constraints and for high relative degree systems. Meanwhile, finding a conservative CBF that only recovers a portion of the true safe set is usually possible. In this work, starting from a "conservative" handcrafted CBF (HCBF), we develop a method to find a CBF that recovers a reasonably larger portion of the safe set. Since the learned CBF controller is not guaranteed to be safe during training iterations, we use a model predictive controller (MPC) to ensure safety during training. Using the collected trajectory data containing safe and unsafe interactions, we train a neural network to estimate the difference between the HCBF and a CBF that recovers a closer solution to the true safe set. With our proposed approach, we can generate safe controllers that are less conservative and computationally more efficient. We validate our approach on two systems: a second-order integrator and a ball-on-beam. © 2022, CC BY.

关键词： Controllers

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Distributed Task Assignment Method for Multiple Robots Based on Dynamic Auction Rules

Distributed Task Assignment Method for Multiple Robots Based...

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Consumer Electronics and computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Jiajie Xu Chinyin Chen Silu Chen Qiang Liu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Ningbo China

Existing researchs on multi-robots task assignment focus on improving the task assignment efficiency without considering the task execution time and ignoring the overall task completion efficiency, which leads to its low computational efficiency in dynamic task assignment scenarios. In addition, the use of a centralized control structure requires high communication quality between robots. To address the above issues, we propose a distributed task assignment method for multiple robots based on dynamic auction rules. The method uses distributed control of robot swarms to share and dynamically update each other's task sets, employs dynamic auction rules for task bidding, adds links to adjust the task execution order, and considers the overall task completion efficiency. Finally, relevant experiments are designed, and the experimental results show that the algorithm is more efficient in terms of distribution and stability, balancing higher execution efficiency and lower motion cost.

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MINER-RRT*: A Hierarchical and Fast Trajectory Planning Framework in 3D Cluttered Environments

arXiv

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arXiv 2024年

作者： Wang, Pengyu Tang, Jiawei Lin, Hin Wang Zhang, Fan Wang, Chaoqun Wang, Jiankun Shi, Ling Meng, Max Q.-H. Shenzhen Key Laboratory of Robotics Perception and Intelligence The Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong The Department of Electrical and Computer Engineering The University of Alberta Canada The School of Control Science and Engineering Shandong University Shandong China

—Trajectory planning for quadrotors in cluttered environments has been challenging in recent years. While many trajectory planning frameworks have been successful, there still exists potential for improvements, particularly in enhancing the speed of generating efficient trajectories. In this paper, we present a novel hierarchical trajectory planning framework to reduce computational time and memory usage called MINER-RRT*, which consists of two main components. First, we propose a sampling-based path planning method boosted by neural networks, where the predicted heuristic region accelerates the convergence of rapidly-exploring random trees. Second, we utilize the optimal conditions derived from the quadrotor’s differential flatness properties to construct polynomial trajectories that minimize control effort in multiple stages. Extensive simulation and real-world experimental results demonstrate that, compared to several state-of-the-art (SOTA) approaches, our method can generate high-quality trajectories with better performance in 3D cluttered environments. (Video1) Note to Practitioners—The motivation is the problem of planning trajectories for quadrotor autonomous flight in 3D cluttered and complex scenarios such as wild forest exploration and subterranean environment search-and-rescue. Sampling-based path planning methods are suitable for dealing with the complexity of the physical environment but are not convenient for computing dynamics and their differentials. Optimization-based trajectory generation methods are appropriate for handling various high-order constraints but rely on high-quality initial path solutions. Therefore, this paper combines the advantages of the two methods to propose a novel trajectory planning framework that can generate high-quality trajectories for quadrotors faster than many previous algorithms. We conduct numerous simulations and real-world experiments to verify that our method can be effectively deployed in real scenarios and empo

关键词： Deep neural networks

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An exact active sensing strategy for a class of bio-inspired systems

arXiv

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arXiv 2024年

作者： Biswas, Debojyoti Sontag, Eduardo D. Cowan, Noah J. Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States Department of Electrical and Computer Engineering Department of Bioengineering Northeastern University BostonMA United States Department of Mechanical Engineering Johns Hopkins University BaltimoreMD United States

We consider a general class of translation-invariant systems with a specific category of output nonlinearities motivated by biological sensing. We show that no dynamic output feedback can stabilize this class of systems to an isolated equilibrium point. To overcome this fundamental limitation, we propose a simple control scheme that includes a low-amplitude periodic forcing function akin to so-called "active sensing" in biology, together with nonlinear output feedback. Our analysis shows that this approach leads to the emergence of an exponentially stable limit cycle. These findings offer a provably stable active sensing strategy and may thus help to rationalize the active sensing movements made by animals as they perform certain motor behaviors. © 2024, CC BY-NC-SA.

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Evading Detection Actively: Toward Anti-Forensics against Forgery Localization

arXiv

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arXiv 2023年

作者： Zhuo, Long Luo, Shenghai Tan, Shunquan Chen, Han Li, Bin Huang, Jiwu The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security Shenzhen Institute of Artificial Intelligence and Robotics for Society China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Anti-forensics seeks to eliminate or conceal traces of tampering artifacts. Typically, anti-forensic methods are designed to deceive binary detectors and persuade them to misjudge the authenticity of an image. However, to the best of our knowledge, no attempts have been made to deceive forgery detectors at the pixel level and mis-locate forged regions. Traditional adversarial attack methods cannot be directly used against forgery localization due to the following defects: 1) they tend to just naively induce the target forensic models to flip their pixel-level pristine or forged decisions;2) their anti-forensics performance tends to be severely degraded when faced with the unseen forensic models;3) they lose validity once the target forensic models are retrained with the anti-forensics images generated by them. To tackle the three defects, we propose SEAR (Self-supErvised Anti-foRensics), a novel self-supervised and adversarial training algorithm that effectively trains deep-learning anti-forensic models against forgery localization. SEAR sets a pretext task to reconstruct perturbation for self-supervised learning. In adversarial training, SEAR employs a forgery localization model as a supervisor to explore tampering features and constructs a deep-learning concealer to erase corresponding traces. We have conducted large-scale experiments across diverse datasets. The experimental results demonstrate that, through the combination of self-supervised learning and adversarial learning, SEAR successfully deceives the state-of-the-art forgery localization methods, as well as tackle the three defects regarding traditional adversarial attack methods mentioned above. Copyright © 2023, The Authors. All rights reserved.

关键词： Defects

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Learning Personalised Models for Automatic Self-Reported Personality Recognition

Learning Personalised Models for Automatic Self-Reported Per...

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ChaLearn LAP Challenge on Understanding Social Behavior in Dyadic and Small Group Interactions Workshop, DYAD 2021, held in conjunction with the International Conference on computer Vision, ICCV 2021

作者： Salam, Hanan Manoranjan, Viswonathan Jiang, Jian Celiktutan, Oya SMART Laboratory Department of Computer Science New York University Abu Dhabi United Arab Emirates SAIR Laboratory Centre for Robotics Research Department of Engineering King’s College London United Kingdom

Previous research has revealed differences in personality traits among different genders, age groups, and even cultures. However, existing methods have focused on one-fits-all approaches only and performed personality recognition without taking into consideration the user’s profiles. In this paper, we propose to learn personalised models of self-reported big five personality traits. Our proposed approach automatically learns deep learning architectures for different user profiles using Neural Architecture Search (NAS) for predicting the Big Five personality traits from multimodal behavioural features. We experiment with two different user profiling criteria, namely, gender and age, and compare the results of our approach with the state-of-the-art methods. Overall, our results show that personalised models improve the performance as compared to the generic model. Particularly, gender-based user profiling combined with bimodal features reduces the prediction error by 0.128, achieving the state-of-the-art performance on the UDIVA dataset. © 2022 H. Salam, V. Manoranjan, J. Jiang & O. Celiktutan.

关键词： User profile

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A Stable Lithium-ion Battery SOH Estimation Framework for Suppressing Measurement Noise with Unknown Distribution

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IEEE Transactions on Transportation Electrification 2025年

作者： Ma, Wentao Xue, Jingsong Li, Yang Guo, Peng Liu, Xinghua Wei, Zhongbao Wang, Yiwen Chen, Badong Xi’an University of Technology School of Electrical Engineering 710048 China Chalmers University of Technology Department of Electrical Engineering Gothenburg41296 Sweden Beijing Institute of Technology National Engineering Laboratory for Electric Vehicles School of Mechanical Engineering Beijing100081 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Institute of artificial intelligence and robotics Xi’an Jiaotong University Xi’an710048 China

Existing methods for estimating the state of health (SOH) of lithium-ion battery (LIB) typically rely on the assumption that the distribution of noise (or outliers) in the measurement data is known. However, this assumption rarely holds true for LIB operating under real-word conditions. This article proposes a stable framework for accurate SOH estimation that accommodates noises with unknown distribution in both measurement data and label values. The framework combines generalized correntropy loss (GCL) with Savitzky-Golay (SG) filter and extreme learning machine (ELM) to obtain measurement data filter named SG-GCL and SOH estimator named generalized ELM (GELM), respectively. The SG-GCL filtering of the measurement data keeps the root mean square error (RMSE) within 0.0365%, and Pearson correlation between extracted feature and SOH improves by 0.4963, which in turn leads to the reduction of the RMSE metrics of the ELM for the estimation of the SOH by 43.69%. From the filtering results, feature extraction and estimation results proved its necessity and effectiveness. GELM effectively suppresses the influence of label value noise on the model in the training process, which reduces the SOH estimation RMSE index by more than 0.66%. The results from experiments with different distributional noise conditions show that the proposed SOH estimation framework has excellent and stable performance. © 2015 IEEE.

关键词： Mean square error

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Physics Representation Learning for Dexterous Manipulation Planning

Physics Representation Learning for Dexterous Manipulation P...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Yi Wu Mengsha Hu Runxiang Jin Rui Liu Informatique et Sciences du Numérique (Computer Research Laboratory) CentraléSupelec Paris-Saclay University Gif-Sur-Yvette France Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Department of Computer Science Kent State University Kent OH USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Dexterous manipulation in robotics, particularly with high degrees of freedom (DoF) devices like the 24-joint Shadow Hand, confronts complexities in search space and execution precision. Humans, however, manipulate objects effortlessly, thanks to their innate grasp of physics. Inspired by this, we introduce the Physics Representation Learning (PRL) framework for robotic hand manipulation. PRL uses physics principles for action conceptualization, such as aligning axes at specific angles (Figure 1). It is a natural language that describes the physics laws, thus bridging the gap between high-level semantics planning and joint-level execution through inverse kinematics (IK). Built on top of the physics-informed action space, PRL deploys a Reinforcement Learning (RL) network with expert demonstrations. Its effectiveness was validated in Nvidia Isaac Sim on four tasks: hammering, unscrewing, sweeping, and pinching. Results show that PRL significantly outperforms conventional joint-control algorithms and RL without demonstrations, underscoring the benefits of physics-based action representations for complex tasks.

关键词： Representation learning Semantics Natural languages Reinforcement learning Kinematics Planning Complexity theory Physics Robots

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FireXnet: an explainable AI-based tailored deep learning model for wildfire detection on resource-constrained devices

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Fire Ecology 2023年第1期19卷 1-19页

作者： Ahmad, Khubab Khan, Muhammad Shahbaz Ahmed, Fawad Driss, Maha Boulila, Wadii Alazeb, Abdulwahab Alsulami, Mohammad Alshehri, Mohammed S. Ghadi, Yazeed Yasin Ahmad, Jawad Faculty of Engineering and Technology Multimedia University Melaka Malaysia School of Computing Engineering and the Built Environment Edinburgh Napier University Edinburgh UK Department of Cyber Security Pakistan Navy Engineering College NUST Karachi Pakistan Robotics and Internet of Things Laboratory Prince Sultan University Riyadh Saudi Arabia RIADI Laboratory National School of Computer Sciences University of Manouba Manouba Tunisia Department of Computer Science College of Computer Science and Information Systems Najran University Najran Saudi Arabia Department of Software Engineering Computer Science Al Ain University Abu Dhabi United Arab Emirates

Forests cover nearly one-third of the Earth’s land and are some of our most biodiverse ecosystems. Due to climate change, these essential habitats are endangered by increasing wildfires. Wildfires are not just a risk to the environment, but they also pose public health risks. Given these issues, there is an indispensable need for efficient and early detection methods. Conventional detection approaches fall short due to spatial limitations and manual feature engineering, which calls for the exploration and development of data-driven deep learning solutions. This paper, in this regard, proposes 'FireXnet', a tailored deep learning model designed for improved efficiency and accuracy in wildfire detection. FireXnet is tailored to have a lightweight architecture that exhibits high accuracy with significantly less training and testing time. It contains considerably reduced trainable and non-trainable parameters, which makes it suitable for resource-constrained devices. To make the FireXnet model visually explainable and trustable, a powerful explainable artificial intelligence (AI) tool, SHAP (SHapley Additive exPlanations) has been incorporated. It interprets FireXnet’s decisions by computing the contribution of each feature to the prediction. Furthermore, the performance of FireXnet is compared against five pre-trained models — VGG16, InceptionResNetV2, InceptionV3, DenseNet201, and MobileNetV2 — to benchmark its efficiency. For a fair comparison, transfer learning and fine-tuning have been applied to the aforementioned models to retrain the models on our dataset. The test accuracy of the proposed FireXnet model is 98.42%, which is greater than all other models used for comparison. Furthermore, results of reliability parameters confirm the model’s reliability, i.e., a confidence interval of [0.97, 1.00] validates the certainty of the proposed model’s estimates and a Cohen’s kappa coefficient of 0.98 proves that decisions of FireXnet are in considerable accordance with t

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