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LiLMaps: Learnable Implicit Language Maps

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arXiv 2025年

作者： Kruzhkov, Evgenii Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI University of Bonn Germany Autonomous Intelligent Systems Computer Science Institute VI – Intelligent Systems and Robotics Center for Robotics and The Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

One of the current trends in robotics is to employ large language models (LLMs) to provide non-predefined command execution and natural human-robot interaction. It is useful to have an environment map together with its language representation, which can be further utilized by LLMs. Such a comprehensive scene representation enables numerous ways of interaction with the map for autonomously operating robots. In this work, we present an approach that enhances incremental implicit mapping through the integration of vision-language features. Specifically, we (i) propose a decoder optimization technique for implicit language maps which can be used when new objects appear on the scene, and (ii) address the problem of inconsistent vision-language predictions between different viewing positions. Our experiments demonstrate the effectiveness of LiLMaps and solid improvements in performance. © 2025, CC BY-NC-ND.

关键词： Visual languages

De-noising of Low Dose CT Liver Images Using Improved Discrete Wavelet Transform 7th

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De-noising of Low Dose CT Liver Images Using Improved Discre...

7th IFIP TC 12 International Conference on Computational intelligence in Data Science, ICCIDS 2024

作者： Maria, H. Heartlin Kayalvizhi, R. Evangeline, I. Keren Princess, T. George Mangalya, T. Rashmika Prakash, J. Shakthi Department of Electronics and Communication Engineering SRM Institute of Science and Technology Kattankulathur Chennai603203 India Department of Robotics and Automation SRM Easwari Engineering College Chennai India Department of Medical Electronics SRM Valliammai Engineering College Kattankulathur India Department of Artificial Intelligence and Datascience St. Joseph’s Institute of Technology Chennai India

ISBN: (纸本)9783031699818

CT scans have become increasingly common in recent years and are essential for diagnosing various medical conditions. But the most significant element influencing a CT image’s quality is noise, which frequently taints low-dose scans. The radiologists’ choice may then be impacted by this. Therefore, in order to enhance visibility and increase the clarity of the image, LDCT images must be removed of noise before being utilized for diagnosis. This work is one such attempt to remove noise from low dose Computed Tomography (CT) liver images. In the present work, the noise from the CT liver images is eliminated using a modified discrete wavelet transform (DWT). The scheme system is used in conjunction with the DWT. The lifting approach improves the wavelet transform’s accuracy, simplicity, efficiency, and flexibility. Furthermore, the noisy wavelet coefficients are denoised via Bayesian shrinkage. The de-noised image PSNR, MSE, SSIM, and SNR values are computed, and a comparison with the traditional de-noising methods is carried out. The quantitative study reveals that the suggested approach has produced noteworthy outcomes when compared to the efficacy of conventional procedures. Furthermore, as the results and discussion section demonstrate, the suggested lifting technique in conjunction with the DWT minimizes the memory needs while simultaneously reducing complexity. © IFIP International Federation for Information Processing 2024.

关键词： Discrete wavelet transforms

KARMA: Augmenting Embodied AI Agents with Long-and-short Term Memory Systems

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arXiv 2024年

作者： Wang, Zixuan Yu, Bo Zhao, Junzhe Sun, Wenhao Hou, Sai Liang, Shuai Hu, Xing Han, Yinhe Gan, Yiming China Shenzhen Institute of Artificial Intelligence and Robotics China Alibaba Group Hangzhou China China Beijing Institute of Technology China University of Chinese Academy of Sciences China

Embodied AI agents responsible for executing interconnected, long-sequence household tasks often face difficulties with in-context memory, leading to inefficiencies and errors in task execution. To address this issue, we introduce KARMA, an innovative memory system that integrates long-term and short-term memory modules, enhancing large language models (LLMs) for planning in embodied agents through memory-augmented prompting. KARMA distinguishes between long-term and short-term memory, with long-term memory capturing comprehensive 3D scene graphs as representations of the environment, while short-term memory dynamically records changes in objects’ positions and states. This dual-memory structure allows agents to retrieve relevant past scene experiences, thereby improving the accuracy and efficiency of task planning. Short-term memory employs strategies for effective and adaptive memory replacement, ensuring the retention of critical information while discarding less pertinent data. Compared to state-of-the-art embodied agents enhanced with memory, our memory-augmented embodied AI agent improves success rates by 1.3x and 2.3x in Composite Tasks and Complex Tasks within the AI2-THOR simulator, respectively, and enhances task execution efficiency by 3.4x and 62.7x. Furthermore, we demonstrate that KARMA’s plug-and-play capability allows for seamless deployment on real-world robotic systems, such as mobile manipulation *** this plug-and-play memory system, KARMA significantly enhances the ability of embodied agents to generate coherent and contextually appropriate plans, making the execution of complex household tasks more efficient. The experimental videos from the work can be found at https://***/4BT7fnw9ehs. Our code is available at https://***/ WZX®@Swarm@robotics/KARMA/tree/master. Copyright © 2024, The Authors. All rights reserved.

关键词： Robot programming

Optimized Control Invariance Conditions for Uncertain Input-Constrained Nonlinear Control Systems

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arXiv 2023年

作者： Brunke, Lukas Zhou, Siqi Che, Mingxuan Schoellig, Angela P. The Learning Systems and Robotics Lab The Technical University of Munich Germany Germany The University of Toronto Institute for Aerospace Studies The University of Toronto Robotics Institute The Vector Institute for Artificial Intelligence Canada

Providing safety guarantees for learning-based controllers is important for real-world applications. One approach to realizing safety for arbitrary control policies is safety filtering. If necessary, the filter modifies control inputs to ensure that the trajectories of a closed-loop system stay within a given state constraint set for all future time, referred to as the set being positive invariant or the system being safe. Under the assumption of fully known dynamics, safety can be certified using control barrier functions (CBFs). However, the dynamics model is often either unknown or only partially known in practice. Learning-based methods have been proposed to approximate the CBF condition for unknown or uncertain systems from data;however, these techniques do not account for input constraints and, as a result, may not yield a valid CBF condition to render the safe set invariant. In this work, we study conditions that guarantee control invariance of the system under input constraints and propose an optimization problem to reduce the conservativeness of CBF-based safety filters. Building on these theoretical insights, we further develop a probabilistic learning approach that allows us to build a safety filter that guarantees safety for uncertain, input-constrained systems with high probability. We demonstrate the efficacy of our proposed approach in simulation and real-world experiments on a quadrotor and show that we can achieve safe closed-loop behavior for a learned system while satisfying state and input constraints. Copyright © 2023, The Authors. All rights reserved.

关键词： Uncertain systems

Nearly Lossless Adaptive Bit Switching

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arXiv 2025年

作者： Huang, Haiduo Liu, Zhenhua Xia, Tian Zhao, Wenzhe Ren, Pengju The National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center of Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an China

Model quantization is widely applied for compressing and accelerating deep neural networks (DNNs). However, conventional Quantization-Aware Training (QAT) focuses on training DNNs with uniform bit-width. The bit-width settings vary across different hardware and transmission demands, which induces considerable training and storage costs. Hence, the scheme of one-shot joint training multiple precisions is proposed to address this issue. Previous works either store a larger FP32 model to switch between different precision models for higher accuracy or store a smaller INT8 model but compromise accuracy due to using shared quantization parameters. In this paper, we introduce the Double Rounding quantization method, which fully utilizes the quantized representation range to accomplish nearly lossless bit-switching while reducing storage by using the highest integer precision instead of full precision. Furthermore, we observe a competitive interference among different precisions during one-shot joint training, primarily due to inconsistent gradients of quantization scales during backward propagation. To tackle this problem, we propose an Adaptive Learning Rate Scaling (ALRS) technique that dynamically adapts learning rates for various precisions to optimize the training process. Additionally, we extend our Double Rounding to one-shot mixed precision training and develop a Hessian-Aware Stochastic Bit-switching (HASB) strategy. Experimental results on the ImageNet-1K classification demonstrate that our methods have enough advantages to state-of-the-art one-shot joint QAT in both multi-precision and mixed-precision. We also validate the feasibility of our method on detection and segmentation tasks, as well as on LLMs task. Our codes are available at https://***/haiduo/Double-Rounding. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep neural networks

Adaptive neural network control of quadrotor unmanned aerial vehicle transportation systems

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Adaptive neural network control of quadrotor unmanned aerial...

2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Liang, Xiao Zhang, Zhuang Yu, Hai Wang, Yang Sun, Ning Nankai University Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Tianjin300350 China

ISBN: (纸本)9781665436786

In practical applications for aerial transportation tasks, owing to the complexity of the environment, the external disturbances are uncertain, which will affect the performance of the control system. This paper studies the control problem of the quadrotor unmanned aerial vehicle (UAV) transportation system with unknown external disturbances. A novel adaptive neural network (NN) control law, based on an artificially constructed sliding manifold and radial basis functions (RBF) NNs, is proposed to achieve stable positioning of the quadrotor without any linearizing operations. Specifically, RBFNN approximators are utilized to compensate for uncertainties/disturbances, and the update law for weights of neural network is designed. By Lyapunov techniques, the stability of system is guaranteed and all the signals in the closed-loop system are proved to be bounded. Simulation results validate the superior performance and robustness of the proposed controller by comparing it with the classical control scheme. © 2021 IEEE.

关键词： Closed loop systems

Continuous query-based data trading 24

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Continuous query-based data trading

2024 ACM SIGMETRICS/IFIP Performance Conference on Measurement and Modeling of Computer Systems, SIGMETRICS/PERFORMANCE 2024

作者： Cheng, Jin Ding, Ningning Lui, John C.S. Huang, Jianwei School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society The Chinese University of Hong Kong Shenzhen Shenzhen China Department of Electrical and Computer Engineering Northwestern University Evanston United States Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Hong Kong

ISBN: (纸本)9798400706240

In the era of big data, traditional data trading methods designed for one-time queries on static databases fail to meet the demands of continuous query-based trading on streaming data, often resulting in repeated and inaccurate charges due to neglecting computation sharing in continuous query execution. To address this, we introduce CQTrade, the first trading framework tailored for continuous queries, which incorporates computation sharing across different time windows and queries, enhancing integration with existing trading systems. Our contributions include a theoretical analysis of computation-sharing techniques, the development of a general optimization problem to maximize seller revenue adaptable to various techniques, and the proposal of a branch-and-price algorithm to handle the problem's complexity. Our evaluation shows that the proposed framework improves the success rate of data trading by 12.8% and boosts the seller's revenue by an average of 28.7%, compared to the one-time query-based data trading methods used in the current data market. © 2024 Copyright held by the owner/author(s).

关键词： Integer programming

FT-TF: A 4D Long-Term Flight Trajectory Prediction Method Based on Transformer

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FT-TF: A 4D Long-Term Flight Trajectory Prediction Method Ba...

Chinese Control Conference (CCC)

作者： Shijin Luo Minghui Zhao Zhenjie Zhao Lun Li Shiyong Zhang Xuebo Zhang Tianjin Key Laboratory of Intelligent Robotics Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Nankai University Tianjin China

The accurate prediction of target aircraft trajectory in the process of air combat can significantly improve the ability of aircraft to gain air superiority. Most of the trajectory prediction methods currently applied in air combat are based on traditional time-series prediction algorithms such as LSTM with short prediction steps, which can not realize the demand for long-term prediction in air combat. To address the issue, a novel prediction method FT-TF is proposed in this paper. FT-TF is a multi-step 4D flight trajectory prediction method based on Transformer applied to further improve the accuracy and practicality of trajectory prediction problems. FT-TF uses the first 30 steps of aircraft historical trajectory data as input and the last 30 steps as output. Flight trajectory data sets of aircraft in air combat are used in the experiment to make the trained model closer to the real air combat scene. The experimental results show that the proposed FT-TF can achieve higher prediction accuracy than LSTM and BP neural network. Related Transformer variants of the algorithm are also added to the experiment for comparison, the results demonstrate that the proposed method achieves the smallest prediction error.

关键词：

Insurance Contract for High Renewable Energy Integration

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Insurance Contract for High Renewable Energy Integration

2022 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2022

作者： Zhao, Dongwei Wang, Hao Huang, Jianwei Lin, Xiaojun Mit Energy Initiative Massachusetts Institute of Technology United States Monash University Department of Data Science and Artificial Intelligence Australia School of Science and Engineering The Chinese University of HongKong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China Elmore Family School of Electrical and Computer Engineering Purdue University United States

ISBN: (数字)9781665432542

ISBN: (纸本)9781665432542

The increasing penetration of renewable energy poses significant challenges to power grid reliability. There have been increasing interests in utilizing financial tools, such as insurance, to help end-users hedge the potential risk of lost load due to renewable energy variability. With insurance, a user pays a premium fee to the utility, so that he will get compensated in case his demand is not fully satisfied. A proper insurance design needs to resolve the following two challenges: (i) users' reliability preference is private information;and (ii) the insurance design is tightly coupled with the renewable energy investment decision. To address these challenges, we adopt the contract theory to elicit users' private reliability preferences, and we study how the utility can jointly optimize the insurance contract and the planning of renewable energy. A key analytical challenge is that the joint optimization of the insurance design and the planning of renewables is non-convex. We resolve this difficulty by revealing important structural properties of the optimal solution, using the help of two benchmark problems: the no-insurance benchmark and the social-optimum benchmark. Compared with the no-insurance benchmark, we prove that the social cost and users' total energy cost are always no larger under the optimal contract. Simulation results show that the largest benefit of the insurance contract is achieved at a medium electricity-bill price together with a low type heterogeneity and a high renewable uncertainty. © 2022 IEEE.

关键词： Insurance