Autonomous vehicles trained through multiagent reinforcement learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with ...
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Autonomous vehicles trained through Multi-Agent Reinforcement learning (MARL) have shown impressive results in many driving scenarios. However, the performance of these trained policies can be impacted when faced with...
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The concept of 3D scene graphs is increasingly recognized as a powerful semantic and hierarchical representation of the environment. Current approaches often address this at a coarse, object-level resolution. In contr...
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We present Robot-centric Pooling (RcP), a novel pooling method designed to enhance end-to-end visuomo-tor policies by enabling differentiation between the robots and similar entities or their surroundings. Given an im...
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ISBN:
(数字)9798350377705
ISBN:
(纸本)9798350377712
We present Robot-centric Pooling (RcP), a novel pooling method designed to enhance end-to-end visuomo-tor policies by enabling differentiation between the robots and similar entities or their surroundings. Given an image-proprioception pair, RcP guides the aggregation of image features by highlighting image regions correlating with the robot’s proprioceptive states, thereby extracting robot-centric image representations for policy learning. Leveraging contrastive learning techniques, RcP integrates seamlessly with existing visuomotor policy learning frameworks and is trained jointly with the policy using the same dataset, requiring no extra data collection involving self-distractors. We evaluate the proposed method with reaching tasks in both simulated and real-world settings. The results demonstrate that RcP significantly enhances the policies’ robustness against various unseen distractors, including self-distractors, positioned at different locations. Additionally, the inherent robot-centric characteristic of RcP enables the learnt policy to be far more resilient to aggressive pixel shifts compared to the baselines. Code available at: https://***/Zheyu-Zhuang/RcP
Tactical decisions in air combat are typically evaluated using experience as a basis. Pilots undergo frequent training in various air combat processes to enhance their combat proficiency and evaluation skills. Having ...
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ISBN:
(数字)9798350357882
ISBN:
(纸本)9798350357899
Tactical decisions in air combat are typically evaluated using experience as a basis. Pilots undergo frequent training in various air combat processes to enhance their combat proficiency and evaluation skills. Having the Situational Awareness (SA) necessary to evaluate the effects of multiple missile threats can often be challenging. This study provides a new method for calculating an aircraft fleet's maneuver flexibility in a Beyond-Visual-Range (BVR) setting. Sustaining a high degree of flexibility is necessary to adapt to unforeseen circumstances in BVR air combat. To do that, we employ Deep Neural Networks (DNN) to capture the result of a high-performance aircraft model in the presence of adversarial BVR missiles. We then modify our approach to calculate the aircraft's maneuverability concerning an opposing fleet, looking at the advantages and disadvantages of several flight formations. Finally, we consider the anticipated threat from an incoming opponent formation and optimize the counter-formation. This methodology offers a more sophisticated comprehension of aircraft maneuver flexibility within a BVR framework and aids in developing flexible and efficient decision-making techniques for air combat.
Visual Object Tracking (VOT) is an attractive and significant research area in computer vision, which aims to recognize and track specific targets in video sequences where the target objects are arbitrary and class-ag...
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Virtual try-on focuses on adjusting the given clothes to fit a specific person seamlessly while avoiding any distortion of the patterns and textures of the garment. However, the clothing identity uncontrollability and...
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Objective and Impact *** this work,we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical *** with the conventional model trained on a...
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Objective and Impact *** this work,we develop a universal anatomical landmark detection model which learns once from multiple datasets corresponding to different anatomical *** with the conventional model trained on a single dataset,this universal model not only is more light weighted and easier to train but also improves the accuracy of the anatomical landmark *** accurate and automatic localization of anatomical landmarks plays an essential role in medical image ***,recent deep learning-based methods only utilize limited data from a single *** is promising and desirable to build a model learned from different regions which harnesses the power of big *** model consists of a local network and a global network,which capture local features and global features,*** local network is a fully convolutional network built up with depth-wise separable convolutions,and the global network uses dilated convolution to enlarge the receptive field to model global *** evaluate our model on four 2D X-ray image datasets totaling 1710 images and 72 landmarks in four anatomical *** experimental results show that our model improves the detection accuracy compared to the state-of-the-art *** model makes the first attempt to train a single network on multiple datasets for landmark *** results qualitatively and quantitatively show that our proposed model performs better than other models trained on multiple datasets and even better than models trained on a single dataset separately.
The sense of touch is essential for robots to perform various daily tasks. Artificial Neural Networks have shown significant promise in advancing robotic tactile learning. However, due to the changing of tactile data ...
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ISBN:
(数字)9798350384574
ISBN:
(纸本)9798350384581
The sense of touch is essential for robots to perform various daily tasks. Artificial Neural Networks have shown significant promise in advancing robotic tactile learning. However, due to the changing of tactile data distribution as robots encounter new tasks, ANN-based robotic tactile learning suffers from catastrophic forgetting. To solve this problem, we introduce a novel continual learning (CL) framework called the Probabilistic Spiking Neural Network with Variational Continual learning (PSNN-VCL). In this framework, PSNN introduces uncertainty during spike emission and can apply fast Variational Inference by optimizing the uncertainty through backpropagation, which significantly reduces the required model parameters for VCL. We establish a robotic tactile CL benchmark using publicly available datasets to evaluate our method. Experimental results demonstrated that, compared to other CL methods, PSNN-VCL not only achieves superior performance in terms of widely used CL metrics but also achieves at least a 50% reduction in model parameters on the robotic tactile CL benchmark.
Recently, Space-Time Memory Network (STM) based methods have achieved state-of-the-art performance in semi-supervised video object segmentation (VOS). A crucial problem in this task is how to model the dependency both...
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