arXiv - CS - Robotics最新文献_第5页

Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching 开放集语义不确定性感知度量-语义图匹配

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11555

Kurran Singh, John J. Leonard

{"title":"Open-Set Semantic Uncertainty Aware Metric-Semantic Graph Matching","authors":"Kurran Singh, John J. Leonard","doi":"arxiv-2409.11555","DOIUrl":"https://doi.org/arxiv-2409.11555","url":null,"abstract":"Underwater object-level mapping requires incorporating visual foundation\u0000models to handle the uncommon and often previously unseen object classes\u0000encountered in marine scenarios. In this work, a metric of semantic uncertainty\u0000for open-set object detections produced by visual foundation models is\u0000calculated and then incorporated into an object-level uncertainty tracking\u0000framework. Object-level uncertainties and geometric relationships between\u0000objects are used to enable robust object-level loop closure detection for\u0000unknown object classes. The above loop closure detection problem is formulated\u0000as a graph-matching problem. While graph matching, in general, is NP-Complete,\u0000a solver for an equivalent formulation of the proposed graph matching problem\u0000as a graph editing problem is tested on multiple challenging underwater scenes.\u0000Results for this solver as well as three other solvers demonstrate that the\u0000proposed methods are feasible for real-time use in marine environments for the\u0000robust, open-set, multi-object, semantic-uncertainty-aware loop closure\u0000detection. Further experimental results on the KITTI dataset demonstrate that\u0000the method generalizes to large-scale terrestrial scenes.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UniLCD: Unified Local-Cloud Decision-Making via Reinforcement Learning UniLCD：通过强化学习进行统一本地云决策

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11403

Kathakoli Sengupta, Zhongkai Shagguan, Sandesh Bharadwaj, Sanjay Arora, Eshed Ohn-Bar, Renato Mancuso

{"title":"UniLCD: Unified Local-Cloud Decision-Making via Reinforcement Learning","authors":"Kathakoli Sengupta, Zhongkai Shagguan, Sandesh Bharadwaj, Sanjay Arora, Eshed Ohn-Bar, Renato Mancuso","doi":"arxiv-2409.11403","DOIUrl":"https://doi.org/arxiv-2409.11403","url":null,"abstract":"Embodied vision-based real-world systems, such as mobile robots, require a\u0000careful balance between energy consumption, compute latency, and safety\u0000constraints to optimize operation across dynamic tasks and contexts. As local\u0000computation tends to be restricted, offloading the computation, ie, to a remote\u0000server, can save local resources while providing access to high-quality\u0000predictions from powerful and large models. However, the resulting\u0000communication and latency overhead has led to limited usability of cloud models\u0000in dynamic, safety-critical, real-time settings. To effectively address this\u0000trade-off, we introduce UniLCD, a novel hybrid inference framework for enabling\u0000flexible local-cloud collaboration. By efficiently optimizing a flexible\u0000routing module via reinforcement learning and a suitable multi-task objective,\u0000UniLCD is specifically designed to support the multiple constraints of\u0000safety-critical end-to-end mobile systems. We validate the proposed approach\u0000using a challenging, crowded navigation task requiring frequent and timely\u0000switching between local and cloud operations. UniLCD demonstrates improved\u0000overall performance and efficiency, by over 35% compared to state-of-the-art\u0000baselines based on various split computing and early exit strategies.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MI-HGNN: Morphology-Informed Heterogeneous Graph Neural Network for Legged Robot Contact Perception MI-HGNN：用于腿部机器人接触感知的形态信息异构图神经网络

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11146

Daniel Butterfield, Sandilya Sai Garimella, Nai-Jen Cheng, Lu Gan

{"title":"MI-HGNN: Morphology-Informed Heterogeneous Graph Neural Network for Legged Robot Contact Perception","authors":"Daniel Butterfield, Sandilya Sai Garimella, Nai-Jen Cheng, Lu Gan","doi":"arxiv-2409.11146","DOIUrl":"https://doi.org/arxiv-2409.11146","url":null,"abstract":"We present a Morphology-Informed Heterogeneous Graph Neural Network (MI-HGNN)\u0000for learning-based contact perception. The architecture and connectivity of the\u0000MI-HGNN are constructed from the robot morphology, in which nodes and edges are\u0000robot joints and links, respectively. By incorporating the morphology-informed\u0000constraints into a neural network, we improve a learning-based approach using\u0000model-based knowledge. We apply the proposed MI-HGNN to two contact perception\u0000problems, and conduct extensive experiments using both real-world and simulated\u0000data collected using two quadruped robots. Our experiments demonstrate the\u0000superiority of our method in terms of effectiveness, generalization ability,\u0000model efficiency, and sample efficiency. Our MI-HGNN improved the performance\u0000of a state-of-the-art model that leverages robot morphological symmetry by 8.4%\u0000with only 0.21% of its parameters. Although MI-HGNN is applied to contact\u0000perception problems for legged robots in this work, it can be seamlessly\u0000applied to other types of multi-body dynamical systems and has the potential to\u0000improve other robot learning frameworks. Our code is made publicly available at\u0000https://github.com/lunarlab-gatech/Morphology-Informed-HGNN.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SDP: Spiking Diffusion Policy for Robotic Manipulation with Learnable Channel-Wise Membrane Thresholds SDP：利用可学习的通道膜阈值实现机器人操纵的尖峰扩散策略

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11195

Zhixing Hou, Maoxu Gao, Hang Yu, Mengyu Yang, Chio-In Ieong

{"title":"SDP: Spiking Diffusion Policy for Robotic Manipulation with Learnable Channel-Wise Membrane Thresholds","authors":"Zhixing Hou, Maoxu Gao, Hang Yu, Mengyu Yang, Chio-In Ieong","doi":"arxiv-2409.11195","DOIUrl":"https://doi.org/arxiv-2409.11195","url":null,"abstract":"This paper introduces a Spiking Diffusion Policy (SDP) learning method for\u0000robotic manipulation by integrating Spiking Neurons and Learnable Channel-wise\u0000Membrane Thresholds (LCMT) into the diffusion policy model, thereby enhancing\u0000computational efficiency and achieving high performance in evaluated tasks.\u0000Specifically, the proposed SDP model employs the U-Net architecture as the\u0000backbone for diffusion learning within the Spiking Neural Network (SNN). It\u0000strategically places residual connections between the spike convolution\u0000operations and the Leaky Integrate-and-Fire (LIF) nodes, thereby preventing\u0000disruptions to the spiking states. Additionally, we introduce a temporal\u0000encoding block and a temporal decoding block to transform static and dynamic\u0000data with timestep $T_S$ into each other, enabling the transmission of data\u0000within the SNN in spike format. Furthermore, we propose LCMT to enable the\u0000adaptive acquisition of membrane potential thresholds, thereby matching the\u0000conditions of varying membrane potentials and firing rates across channels and\u0000avoiding the cumbersome process of manually setting and tuning hyperparameters.\u0000Evaluating the SDP model on seven distinct tasks with SNN timestep $T_S=4$, we\u0000achieve results comparable to those of the ANN counterparts, along with faster\u0000convergence speeds than the baseline SNN method. This improvement is\u0000accompanied by a reduction of 94.3% in dynamic energy consumption estimated on\u000045nm hardware.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Annealed Winner-Takes-All for Motion Forecasting 运动预测中的退火胜者为王

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11172

Yihong Xu, Victor Letzelter, Mickaël Chen, Éloi Zablocki, Matthieu Cord

{"title":"Annealed Winner-Takes-All for Motion Forecasting","authors":"Yihong Xu, Victor Letzelter, Mickaël Chen, Éloi Zablocki, Matthieu Cord","doi":"arxiv-2409.11172","DOIUrl":"https://doi.org/arxiv-2409.11172","url":null,"abstract":"In autonomous driving, motion prediction aims at forecasting the future\u0000trajectories of nearby agents, helping the ego vehicle to anticipate behaviors\u0000and drive safely. A key challenge is generating a diverse set of future\u0000predictions, commonly addressed using data-driven models with Multiple Choice\u0000Learning (MCL) architectures and Winner-Takes-All (WTA) training objectives.\u0000However, these methods face initialization sensitivity and training\u0000instabilities. Additionally, to compensate for limited performance, some\u0000approaches rely on training with a large set of hypotheses, requiring a\u0000post-selection step during inference to significantly reduce the number of\u0000predictions. To tackle these issues, we take inspiration from annealed MCL, a\u0000recently introduced technique that improves the convergence properties of MCL\u0000methods through an annealed Winner-Takes-All loss (aWTA). In this paper, we\u0000demonstrate how the aWTA loss can be integrated with state-of-the-art motion\u0000forecasting models to enhance their performance using only a minimal set of\u0000hypotheses, eliminating the need for the cumbersome post-selection step. Our\u0000approach can be easily incorporated into any trajectory prediction model\u0000normally trained using WTA and yields significant improvements. To facilitate\u0000the application of our approach to future motion forecasting models, the code\u0000will be made publicly available upon acceptance:\u0000https://github.com/valeoai/MF_aWTA.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The 1st InterAI Workshop: Interactive AI for Human-centered Robotics 第 1 届 InterAI 研讨会：以人为中心的机器人交互式人工智能

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11150

Yuchong Zhang, Elmira Yadollahi, Yong Ma, Di Fu, Iolanda Leite, Danica Kragic

引用次数: 0

Air-FAR: Fast and Adaptable Routing for Aerial Navigation in Large-scale Complex Unknown Environments Air-FAR：在大规模复杂未知环境中为空中导航提供快速、自适应的路由选择

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11188

Botao He, Guofei Chen, Cornelia Fermuller, Yiannis Aloimonos, Ji Zhang

引用次数: 0

3D Water Quality Mapping using Invariant Extended Kalman Filtering for Underwater Robot Localization 利用不变扩展卡尔曼滤波进行水下机器人定位的 3D 水质绘图

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11578

Kaustubh Joshi, Tianchen Liu, Alan Williams, Matthew Gray, Xiaomin Lin, Nikhil Chopra

引用次数: 0

Resilient and Adaptive Replanning for Multi-Robot Target Tracking with Sensing and Communication Danger Zones 利用传感和通信危险区为多机器人目标跟踪进行弹性和自适应重新规划

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11230

Peihan Li, Yuwei Wu, Jiazhen Liu, Gaurav S. Sukhatme, Vijay Kumar, Lifeng Zhou

{"title":"Resilient and Adaptive Replanning for Multi-Robot Target Tracking with Sensing and Communication Danger Zones","authors":"Peihan Li, Yuwei Wu, Jiazhen Liu, Gaurav S. Sukhatme, Vijay Kumar, Lifeng Zhou","doi":"arxiv-2409.11230","DOIUrl":"https://doi.org/arxiv-2409.11230","url":null,"abstract":"Multi-robot collaboration for target tracking presents significant challenges\u0000in hazardous environments, including addressing robot failures, dynamic\u0000priority changes, and other unpredictable factors. Moreover, these challenges\u0000are increased in adversarial settings if the environment is unknown. In this\u0000paper, we propose a resilient and adaptive framework for multi-robot,\u0000multi-target tracking in environments with unknown sensing and communication\u0000danger zones. The damages posed by these zones are temporary, allowing robots\u0000to track targets while accepting the risk of entering dangerous areas. We\u0000formulate the problem as an optimization with soft chance constraints, enabling\u0000real-time adjustments to robot behavior based on varying types of dangers and\u0000failures. An adaptive replanning strategy is introduced, featuring different\u0000triggers to improve group performance. This approach allows for dynamic\u0000prioritization of target tracking and risk aversion or resilience, depending on\u0000evolving resources and real-time conditions. To validate the effectiveness of\u0000the proposed method, we benchmark and evaluate it across multiple scenarios in\u0000simulation and conduct several real-world experiments.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

VertiEncoder: Self-Supervised Kinodynamic Representation Learning on Vertically Challenging Terrain VertiEncoder：在垂直挑战性地形上进行自我监督的动力学表征学习

arXiv - CS - Robotics Pub Date : 2024-09-17 DOI: arxiv-2409.11570

Mohammad Nazeri, Aniket Datar, Anuj Pokhrel, Chenhui Pan, Garrett Warnell, Xuesu Xiao

引用次数: 0