Results 101 to 110 of about 3,240,231 (313)

A State‐Adaptive Koopman Control Framework for Real‐Time Deformable Tool Manipulation in Robotic Environmental Swabbing

Advanced Robotics Research, EarlyView.
This work presents a state‐adaptive Koopman linear quadratic regulator framework for real‐time manipulation of a deformable swab tool in robotic environmental sampling. By combining Koopman linearization, tactile sensing, and centroid‐based force regulation, the system maintains stable contact forces and high coverage across flat and inclined surfaces.
Siavash Mahmoudi, Amirreza Davar, Dongyi Wang   +2 more
wiley   +1 more source

PEGAT: Prediction Error-Guided Adversarial Training to Enhance Robustness of Deep Learning Models in Autonomous Vehicles

IEEE Access
Adversarial training is a widely used method to improve the robustness of deep learning models in various applications. Although adversarial training enhances the robustness of the target model, it also suffers from an accuracy versus robustness trade ...
Manzoor Hussain, Zhengyu Shang, Ahmed Dawod Mohammed Ibrahum, Jang-Eui Hong   +3 more
doaj   +1 more source

Learning Lipschitz Feedback Policies From Expert Demonstrations: Closed-Loop Guarantees, Robustness and Generalization

IEEE Open Journal of Control Systems, 2022
In this work, we propose a framework in which we use a Lipschitz-constrained loss minimization scheme to learn feedback control policies with guarantees on closed-loop stability, adversarial robustness, and generalization.
Abed AlRahman Al Makdah, Vishaal Krishnan, Fabio Pasqualetti   +2 more
doaj   +1 more source

Adversaries With Incentives: A Strategic Alternative to Adversarial Robustness

CoRR
Adversarial training aims to defend against adversaries: malicious opponents whose sole aim is to harm predictive performance in any way possible. This presents a rather harsh perspective, which we assert results in unnecessarily conservative training.
Maayan Ehrenberg, Roy Ganz, Nir Rosenfeld   +2 more
openaire   +4 more sources

Wavelets Beat Monkeys at Adversarial Robustness [PDF]

, 2023
Research on improving the robustness of neural networks to adversarial noise - imperceptible malicious perturbations of the data - has received significant attention.
Kempe, Julia, Su, Jingtong
core   +1 more source

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source

A Comparative Study on the Performance and Security Evaluation of Spiking Neural Networks

IEEE Access, 2022
The brain-inspired Spiking neural networks (SNN) claim to present advantages for visual classification tasks in terms of energy efficiency and inherent robustness.
Yanjie Li, Xiaoxin Cui, Yihao Zhou, Ying Li   +3 more
doaj   +1 more source

Stratified Adversarial Robustness with Rejection

CoRR, 2023
Recently, there is an emerging interest in adversarially training a classifier with a rejection option (also known as a selective classifier) for boosting adversarial robustness. While rejection can incur a cost in many applications, existing studies typically associate zero cost with rejecting perturbed inputs, which can result in the rejection of ...
Jiefeng Chen 0001, Jayaram Raghuram, Jihye Choi, Xi Wu 0001, Yingyu Liang, Somesh Jha   +5 more
openaire   +4 more sources

Solid Harmonic Wavelet Bispectrum for Image Analysis

Advanced Science, EarlyView.
The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown, Mathilda Avirett‐Mackenzie, Carolin Villforth, Georgios Exarchakis   +3 more
wiley   +1 more source

Avoiding catastrophic overfitting in fast adversarial training with adaptive similarity step size.

PLoS ONE
Adversarial training has become a primary method for enhancing the robustness of deep learning models. In recent years, fast adversarial training methods have gained widespread attention due to their lower computational cost.
Jie-Chao Zhao, Jin Ding, Yong-Zhi Sun, Ping Tan, Ji-En Ma, You-Tong Fang   +5 more
doaj   +1 more source