arXiv - CS - Robotics最新文献_第3页

Repeatable Energy-Efficient Perching for Flapping-Wing Robots Using Soft Grippers 使用软爪为扇翼机器人提供可重复的高能效栖息地

arXiv - CS - Robotics Pub Date : 2024-09-18 DOI: arxiv-2409.11921

Krispin C. V. Broers, Sophie F. Armanini

{"title":"Repeatable Energy-Efficient Perching for Flapping-Wing Robots Using Soft Grippers","authors":"Krispin C. V. Broers, Sophie F. Armanini","doi":"arxiv-2409.11921","DOIUrl":"https://doi.org/arxiv-2409.11921","url":null,"abstract":"With the emergence of new flapping-wing micro aerial vehicle (FWMAV) designs,\u0000a need for extensive and advanced mission capabilities arises. FWMAVs try to\u0000adapt and emulate the flight features of birds and flying insects. While\u0000current designs already achieve high manoeuvrability, they still almost\u0000entirely lack perching and take-off abilities. These capabilities could, for\u0000instance, enable long-term monitoring and surveillance missions, and operations\u0000in cluttered environments or in proximity to humans and animals. We present the\u0000development and testing of a framework that enables repeatable perching and\u0000take-off for small to medium-sized FWMAVs, utilising soft, non-damaging\u0000grippers. Thanks to its novel active-passive actuation system, an\u0000energy-conserving state can be achieved and indefinitely maintained while the\u0000vehicle is perched. A prototype of the proposed system weighing under 39 g was\u0000manufactured and extensively tested on a 110 g flapping-wing robot. Successful\u0000free-flight tests demonstrated the full mission cycle of landing, perching and\u0000subsequent take-off. The telemetry data recorded during the flights yields\u0000extensive insight into the system's behaviour and is a valuable step towards\u0000full automation and optimisation of the entire take-off and landing cycle.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266826","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

Particle-based Instance-aware Semantic Occupancy Mapping in Dynamic Environments 动态环境中基于粒子的实例感知语义占用映射

arXiv - CS - Robotics Pub Date : 2024-09-18 DOI: arxiv-2409.11975

Gang Chen, Zhaoying Wang, Wei Dong, Javier Alonso-Mora

{"title":"Particle-based Instance-aware Semantic Occupancy Mapping in Dynamic Environments","authors":"Gang Chen, Zhaoying Wang, Wei Dong, Javier Alonso-Mora","doi":"arxiv-2409.11975","DOIUrl":"https://doi.org/arxiv-2409.11975","url":null,"abstract":"Representing the 3D environment with instance-aware semantic and geometric\u0000information is crucial for interaction-aware robots in dynamic environments.\u0000Nonetheless, creating such a representation poses challenges due to sensor\u0000noise, instance segmentation and tracking errors, and the objects' dynamic\u0000motion. This paper introduces a novel particle-based instance-aware semantic\u0000occupancy map to tackle these challenges. Particles with an augmented instance\u0000state are used to estimate the Probability Hypothesis Density (PHD) of the\u0000objects and implicitly model the environment. Utilizing a State-augmented\u0000Sequential Monte Carlo PHD (S$^2$MC-PHD) filter, these particles are updated to\u0000jointly estimate occupancy status, semantic, and instance IDs, mitigating\u0000noise. Additionally, a memory module is adopted to enhance the map's\u0000responsiveness to previously observed objects. Experimental results on the\u0000Virtual KITTI 2 dataset demonstrate that the proposed approach surpasses\u0000state-of-the-art methods across multiple metrics under different noise\u0000conditions. Subsequent tests using real-world data further validate the\u0000effectiveness of the proposed approach.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":"198 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142267037","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

An Efficient Projection-Based Next-best-view Planning Framework for Reconstruction of Unknown Objects 基于投影的高效下一最佳视角规划框架，用于重建未知物体

arXiv - CS - Robotics Pub Date : 2024-09-18 DOI: arxiv-2409.12096

Zhizhou Jia, Shaohui Zhang, Qun Hao

{"title":"An Efficient Projection-Based Next-best-view Planning Framework for Reconstruction of Unknown Objects","authors":"Zhizhou Jia, Shaohui Zhang, Qun Hao","doi":"arxiv-2409.12096","DOIUrl":"https://doi.org/arxiv-2409.12096","url":null,"abstract":"Efficiently and completely capturing the three-dimensional data of an object\u0000is a fundamental problem in industrial and robotic applications. The task of\u0000next-best-view (NBV) planning is to infer the pose of the next viewpoint based\u0000on the current data, and gradually realize the complete three-dimensional\u0000reconstruction. Many existing algorithms, however, suffer a large computational\u0000burden due to the use of ray-casting. To address this, this paper proposes a\u0000projection-based NBV planning framework. It can select the next best view at an\u0000extremely fast speed while ensuring the complete scanning of the object.\u0000Specifically, this framework refits different types of voxel clusters into\u0000ellipsoids based on the voxel structure.Then, the next best view is selected\u0000from the candidate views using a projection-based viewpoint quality evaluation\u0000function in conjunction with a global partitioning strategy. This process\u0000replaces the ray-casting in voxel structures, significantly improving the\u0000computational efficiency. Comparative experiments with other algorithms in a\u0000simulation environment show that the framework proposed in this paper can\u0000achieve 10 times efficiency improvement on the basis of capturing roughly the\u0000same coverage. The real-world experimental results also prove the efficiency\u0000and feasibility of the framework.","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142267029","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

XP-MARL: Auxiliary Prioritization in Multi-Agent Reinforcement Learning to Address Non-Stationarity XP-MARL：多代理强化学习中的辅助优先级以解决非稳定性问题

arXiv - CS - Robotics Pub Date : 2024-09-18 DOI: arxiv-2409.11852

Jianye Xu, Omar Sobhy, Bassam Alrifaee

{"title":"XP-MARL: Auxiliary Prioritization in Multi-Agent Reinforcement Learning to Address Non-Stationarity","authors":"Jianye Xu, Omar Sobhy, Bassam Alrifaee","doi":"arxiv-2409.11852","DOIUrl":"https://doi.org/arxiv-2409.11852","url":null,"abstract":"Non-stationarity poses a fundamental challenge in Multi-Agent Reinforcement\u0000Learning (MARL), arising from agents simultaneously learning and altering their\u0000policies. This creates a non-stationary environment from the perspective of\u0000each individual agent, often leading to suboptimal or even unconverged learning\u0000outcomes. We propose an open-source framework named XP-MARL, which augments\u0000MARL with auxiliary prioritization to address this challenge in cooperative\u0000settings. XP-MARL is 1) founded upon our hypothesis that prioritizing agents\u0000and letting higher-priority agents establish their actions first would\u0000stabilize the learning process and thus mitigate non-stationarity and 2)\u0000enabled by our proposed mechanism called action propagation, where\u0000higher-priority agents act first and communicate their actions, providing a\u0000more stationary environment for others. Moreover, instead of using a predefined\u0000or heuristic priority assignment, XP-MARL learns priority-assignment policies\u0000with an auxiliary MARL problem, leading to a joint learning scheme. Experiments\u0000in a motion-planning scenario involving Connected and Automated Vehicles (CAVs)\u0000demonstrate that XP-MARL improves the safety of a baseline model by 84.4% and\u0000outperforms a state-of-the-art approach, which improves the baseline by only\u000012.8%. Code: github.com/cas-lab-munich/sigmarl","PeriodicalId":501031,"journal":{"name":"arXiv - CS - Robotics","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142266830","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

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":"32 1","pages":""},"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":"28 1","pages":""},"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":"98 1","pages":""},"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":"64 1","pages":""},"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":"9 1","pages":""},"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

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