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IEEE International Conference on robotics and Automation (ICRA)

作者： Zach J. Patterson Cosimo Della Santina Daniela Rus Computer Science and Artificial Intelligence Laboratory MIT Department of Cognitive Robotics Delft University of Technology Institute of Robotics and Mechatronics German Aerospace Center (DLR)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

While much work has been done recently in the realm of model-based control of soft robots and soft-rigid hybrids, most works examine robots that have an inherently serial structure. While these systems have been prevalent in the literature, there is an increasing trend toward designing soft-rigid hybrids with intrinsically coupled elasticity between various degrees of freedom. In this work, we seek to address the issues of modeling and controlling such structures, particularly when underactuated. We introduce several simple models for elastic coupling, typical of those seen in these systems. We then propose a controller that compensates for the elasticity, and we prove its stability with Lyapunov methods without relying on the elastic dominance assumption. This controller is applicable to the general class of underactuated soft robots. After evaluating the controller in simulated cases, we then develop a simple hardware platform to evaluate both the models and the controller. Finally, using the hardware, we demonstrate a novel use case for underactuated, elastically coupled systems in "sensorless" force control.

关键词： Couplings Elasticity Soft robotics Robot sensing systems Market research Hardware Stability analysis

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LiLMaps: Learnable Implicit Language Maps

arXiv

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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

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RL-Based Control of UAS Subject to Significant Disturbance

RL-Based Control of UAS Subject to Significant Disturbance

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Kousheek Chakraborty Thijs Hof Ayham Alharbat Abeje Mersha Smart Mechatronics and Robotics research group Saxion University of Applied Science Enschede The Netherlands Robotics and Mechatronics research group Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Enschede The Netherlands

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This paper proposes a Reinforcement Learning (RL)-based control framework for position and attitude control of an Unmanned Aerial System (UAS) subjected to significant disturbance that can be associated with an uncertain trigger signal. The proposed method learns the relationship between the trigger signal and disturbance force, enabling the system to anticipate and counteract the impending disturbances before they occur. We train and evaluate three policies: a baseline policy trained without exposure to the disturbance, a reactive policy trained with the disturbance but without the trigger signal, and a predictive policy that incorporates the trigger signal as an observation and is exposed to the disturbance during training. Our simulation results show that the predictive policy outperforms the other policies by minimizing position deviations through a proactive correction maneuver. This work highlights the potential of integrating predictive cues into RL frameworks to improve UAS performance.

关键词： Training Attitude control Simulation Force Reinforcement learning Autonomous aerial vehicles Stability analysis Maintenance Dynamical systems Logistics

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IoT Intervention in the monitoring of aquaponics metrics and control of input schedule

IoT Intervention in the monitoring of aquaponics metrics and...

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2024 International Conference on Smart Devices, ICSD 2024

作者： Malik, Pooja Malik, Praveen Kumar Ranjith Kumar, Gotte Bali, Jyoti S. Mahajan, Anshul Muhsen Hassan, M. Lovely Professional University School of Electronics and Electrical Engineering Punjab Phagwara India SR University School of Computer Science & Artificial Intelligence Warangal India Dept of Robotics and Automation Pune India The Islamic University Najaf Iraq

ISBN: (纸本)9798350394412

The extensive use of technology across several Modern civilization is clearly impacted by technology, with cutting-edge techniques and tools playing critical roles in sectors including healthcare, business, agriculture, and education. Processes have been transformed by these improvements, which have increased their dependability and efficiency. With the incorporation of technology, agriculture has made enormous strides, with techniques like aquaponics developing as extremely effective and sustainable systems. Aquaponics is a novel farming method that combines hydroponics (soilless plant cultivation) and aquaculture (fish farming) to grow vegetables and fish at the same time. But the performance of this incorporated system relies upon on careful observation of a number of vital elements, such as water high-quality, pH tiers, temperature, and dissolved oxygen levels. To keep the health and growth of both fish and flora in an aquaponics device, it's miles critical to hold perfect situations. This highlights the significance of technological intervention, especially via the integration of Internet of Things (IoT) era, and calls for precision in regulating the input time table for fish feed and plant vitamins. © 2024 IEEE.

关键词： Fish

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PhotoReg: Photometrically Registering 3D Gaussian Splatting Models

arXiv

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arXiv 2024年

作者： Yuan, Ziwen Zhang, Tianyi Johnson-Roberson, Matthew Zhi, Weiming Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States

Building accurate representations of the environment is critical for intelligent robots to make decisions during deployment. Advances in photorealistic environment models have enabled robots to develop hyper-realistic reconstructions, which can be used to generate images that are intuitive for human inspection. In particular, the recently introduced 3D Gaussian Splatting (3DGS), which describes the scene with up to millions of primitive ellipsoids, can be rendered in real time. 3DGS has rapidly gained prominence. However, a critical unsolved problem persists: how can we fuse multiple 3DGS into a single coherent model? Solving this problem will enable robot teams to jointly build 3DGS models of their surroundings. A key insight of this work is to leverage the duality between photorealistic reconstructions, which render realistic 2D images from 3D structure, and 3D foundation models, which predict 3D structure from image pairs. To this end, we develop PhotoReg, a framework to register multiple photorealistic 3DGS models with 3D foundation models. As 3DGS models are generally built from monocular camera images, they have arbitrary scale. To resolve this, PhotoReg actively enforces scale consistency among the different 3DGS models by considering depth estimates within these models. Then, the alignment is iteratively refined with fine-grained photometric losses to produce high-quality fused 3DGS models. We rigorously evaluate PhotoReg on both standard benchmark datasets and our custom-collected datasets, including with two quadruped robots. The code is released at https://***/photoreg. © 2024, CC BY.

关键词： Digital elevation model

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PointPillars Backbone Type Selection for Fast and Accurate LiDAR Object Detection

PointPillars Backbone Type Selection for Fast and Accurate L...

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International Conference on computer Vision and Graphics, ICCVG 2022

作者： Lis, Konrad Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783031220241

3D object detection from LiDAR sensor data is an important topic in the context of autonomous cars and drones. In this paper, we present the results of experiments on the impact of backbone selection of a deep convolutional neural network on detection accuracy and computation speed. We chose the PointPillars network, which is characterised by a simple architecture, high speed, and modularity that allows for easy expansion. During the experiments, we paid particular attention to the change in detection efficiency (measured by the mAP metric) and the total number of multiply-addition operations needed to process one point cloud. We tested 10 different convolutional neural network architectures that are widely used in image-based detection problems. For a backbone like MobilenetV1, we obtained an almost 4x speedup at the cost of a 1.13% decrease in mAP. On the other hand, for CSPDarknet we got an acceleration of more than 1.5x at an increase in mAP of 0.33%. We have thus demonstrated that it is possible to significantly speed up a 3D object detector in LiDAR point clouds with a small decrease in detection efficiency. This result can be used when PointPillars or similar algorithms are implemented in embedded systems, including SoC FPGAs. The code is available at https://***/vision-agh/pointpillars-backbone. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Active Multi-Modal Approach for Enhanced User Recognition in Social Robots

Active Multi-Modal Approach for Enhanced User Recognition in...

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Western New York Image Processing Workshop (WNYIPW)

作者： Ninad Kale Nalini Ratha Robotics Lab University at Buffalo Computer Science and Engineering University at Buffalo

The field of Human-Robot Interaction (HRI) is swiftly expanding, driven by notable advancements in artificial intelligence (AI). Humanoid robots, now capable of being equipped with advanced AI models, are being considered for a wide array of applications due to their ability to perform complex tasks and interact with humans in both natural and intelligent ways. In the domain of social robotics, the capability to selectively interact with authorized users is crucial for ensuring security and providing personalized user experiences. Unimodal user recognition methods, such as audio-based user recognition, are commonly used in social robots. However, these methods can be susceptible to ambient noise and might exhibit reduced accuracy. Although face recognition modality is often employed to enhance accuracy, the majority of audio-visual person recognition methods are trained on datasets with only a single user in the *** paper introduces a method for audio-visual user recognition with multiple users in the frame, utilizing an additional sound localization modality. The proposed method is evaluated using a dataset created from interactions between the social robot Pepper and multiple users. The results demonstrated that the proposed method significantly outperformed unimodal user recognition methods.

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Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks 38

Model LEGO: Creating Models Like Disassembling and Assemblin...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Hu, Jiacong Gao, Jing Ye, Jingwen Gao, Yang Wang, Xingen Feng, Zunlei Song, Mingli College of Computer Science and Technology Zhejiang University China Robotics Institute School of Computer Science Carnegie Mellon University United States Electrical and Computer Engineering National University of Singapore Singapore School of Software Technology Zhejiang University China State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China Bangsheng Technology Co. Ltd. China

With the rapid development of deep learning, the increasing complexity and scale of parameters make training a new model increasingly resource-intensive. In this paper, we start from the classic convolutional neural network (CNN) and explore a paradigm that does not require training to obtain new models. Similar to the birth of CNN inspired by receptive fields in the biological visual system, we draw inspiration from the information subsystem pathways in the biological visual system and propose Model Disassembling and Assembling (MDA). During model disassembling, we introduce the concept of relative contribution and propose a component locating technique to extract task-aware components from trained CNN classifiers. For model assembling, we present the alignment padding strategy and parameter scaling strategy to construct a new model tailored for a specific task, utilizing the disassembled task-aware components. The entire process is akin to playing with LEGO bricks, enabling arbitrary assembly of new models, and providing a novel perspective for model creation and reuse. Extensive experiments showcase that task-aware components disassembled from CNN classifiers or new models assembled using these components closely match or even surpass the performance of the baseline, demonstrating its promising results for model reuse. Furthermore, MDA exhibits diverse potential applications, with comprehensive experiments exploring model decision route analysis, model compression, knowledge distillation, and more. For more information, please visit https://***/. © 2024 Neural information processing systems foundation. All rights reserved.

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SelfMedHPM: Self Pre-Training with Hard Patches Mining Masked Autoencoders for Medical Image Segmentation 22

SelfMedHPM: Self Pre-Training with Hard Patches Mining Maske...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Lv, Yunhao Chen, Lingyu Wang, Jian Li, Yangxi Chen, Fang Nanjing University of Aeronautics and Astronautics Department of Computer Science and Engineering Nanjing China School of Biomedical Engineering Institute of Medical Robotics Shanghai JiaoTong University Shanghai200240 China

ISBN: (纸本)9798331520526

In recent years, deep learning methods such as convolutional neural network (CNN) and transformers have made significant progress in CT multi-organ segmentation. However, CT multi-organ segmentation methods based on masked image modeling (MIM) are very limited. There are already methods using MAE for CT multi-organ segmentation task, we believe that the existing methods do not identify the most difficult areas to reconstruct. To this end, we propose a MIM self-training framework with hard patches mining masked autoencoders for CT multi-organ segmentation tasks (self-MedHPM). The method performs ViT self-pretraining on the training set of the target data and introduces an auxiliary loss predictor, which first predicts the patch loss and determines the location of the next mask. SelfMedHPM implementation is better than various competitive methods in abdominal CT multi-organ segmentation and body CT multi-organ segmentation. We have validated the performance of our method on the Multi Atlas Labeling Beyond The Cranial Vault (BTCV) dataset for abdomen mult-organ segmentation and the SinoMed Whole Body (SMWB) dataset for body multi-organ segmentation tasks. © 2025 IEEE.

关键词： Convolutional neural networks

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(POSTER) Drone Detection and Localization Using Low-Cost Microphone Arrays and Convolutional Neural Networks

(POSTER) Drone Detection and Localization Using Low-Cost Mic...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Peteris Racinskis Janis Arents Modris Greitans Robotics and Machine Perception Laboratory Institute of Electronics and Computer Science Latvia

This paper examines the possibility of using low-cost commercial off-the-shelf audio recording equipment in combination with machine learning techniques to discover the presence of hostile UAVs. A convolutional neural network (CNN) was trained to detect and localize two types of quadrotor drones using ground truth position data collected with motion capture equipment. System performance was evaluated on pre-recorded validation data sets and in realtime operation. In both cases, drones can be successfully detected and localized within the constrained working volumes studied, achieving angular accuracies in the 8–13° range. However, further work remains to be done before system feasibility in outdoor conditions can be established.

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