2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)最新文献_第2页

Centralized-Equivalent Pairwise Estimation with Asynchronous Communication Constraints for two Robots 两机器人异步通信约束下的集中等效两两估计

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2022-10-23 DOI: 10.1109/IROS47612.2022.9982117

Eren Allak, A. Barrau, R. Jung, J. Steinbrener, Stephan Weiss

{"title":"Centralized-Equivalent Pairwise Estimation with Asynchronous Communication Constraints for two Robots","authors":"Eren Allak, A. Barrau, R. Jung, J. Steinbrener, Stephan Weiss","doi":"10.1109/IROS47612.2022.9982117","DOIUrl":"https://doi.org/10.1109/IROS47612.2022.9982117","url":null,"abstract":"Collaboratively estimating the state of two robots under communication constraints is challenging regarding computational complexity and statistical optimality. Previous work only achieves practical solutions by either disregarding parts of the measurements or imposing a communication overhead, being non-optimal or not entirely distributed, respectively. In this work, we present a centralized-equivalent but dis-tributed approach for pairwise state estimation where two agents only communicate when they meet. Our approach utilizes elements from wave scattering theory to efficiently and consistently summarize (pre-compute) past estimator information (i.e., state evolution and uncertainty) between encounters of two agents. This summarized information is then used in a joint correction step taking into account all past information of each agent in a statistically correct way. This novel approach enables us to distribute the pre-computations of both state evolution and uncertainties on the agents and reconstruct the centralized-equivalent system estimate with very few computations once the agents meet again while still applying all measurements from both agents on both estimates upon encounter. We compare our approach on a real-world dataset against a state of the art collaborative state estimation approach.","PeriodicalId":431373,"journal":{"name":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126036772","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: 2

Ω2: Optimal Hierarchical Planner for Object Search in Large Environments via Mobile Manipulation Ω2:通过移动操作的大型环境中对象搜索的最优分层规划器

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2022-10-23 DOI: 10.1109/IROS47612.2022.9981194

Yoon-ok Cho, Donghoon Shin, Beomjoon Kim

{"title":"Ω2: Optimal Hierarchical Planner for Object Search in Large Environments via Mobile Manipulation","authors":"Yoon-ok Cho, Donghoon Shin, Beomjoon Kim","doi":"10.1109/IROS47612.2022.9981194","DOIUrl":"https://doi.org/10.1109/IROS47612.2022.9981194","url":null,"abstract":"We propose a hierarchical planning algorithm that efficiently computes an optimal plan for finding a target object in large environments where a robot must simultaneously consider both navigation and manipulation. One key challenge that arises from large domains is the substantial increase in search space complexity that stems from considering mobile manipulation actions and the increase in number of objects. We offer a hierarchical planning solution that effectively handles such large problems by decomposing the problem into a set of low-level intra-container planning problems and a high-level key place planning problem that utilizes the low-level plans. To plan optimally, we propose a novel admissible heuristic function that, unlike previous methods, accounts for both navigation and manipulation costs. We propose two algorithms: one based on standard A* that returns the optimal solution, and the other based on Anytime Repairing A* (ARA*) which can trade-off computation time and solution quality, and prove they are optimal even when we use hierarchy. We show our method outperforms existing algorithms in simulated domains involving up to 6 times more number of objects than previously handled.","PeriodicalId":431373,"journal":{"name":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123526715","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

Compact Strawberry Harvesting Tube Employing Laser Cutter 紧凑草莓收获管采用激光切割机

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2022-10-23 DOI: 10.1109/IROS47612.2022.9981720

Mohamed Sorour, P. From, K. Elgeneidy, S. Kanarachos, M. Sallam

引用次数: 0

Improving Single-View Mesh Reconstruction for Unseen Categories via Primitive-Based Representation and Mesh Augmentation 通过基于原语的表示和网格增强改进未见分类的单视图网格重建

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2022-10-23 DOI: 10.1109/IROS47612.2022.9982024

Yu-Liang Kuo, Wei-Chen Chiu

{"title":"Improving Single-View Mesh Reconstruction for Unseen Categories via Primitive-Based Representation and Mesh Augmentation","authors":"Yu-Liang Kuo, Wei-Chen Chiu","doi":"10.1109/IROS47612.2022.9982024","DOIUrl":"https://doi.org/10.1109/IROS47612.2022.9982024","url":null,"abstract":"As most existing works of single-view 3D reconstruction aim at learning the better mapping functions to directly transform the 2D observation into the corresponding 3D shape for achieving state-of-the-art performance, there often comes a potential concern on having the implicit bias towards the seen classes learnt in their models (i.e. reconstruction intertwined with the classification) thus leading to poor generalizability for the unseen object categories. Moreover, such implicit bias typically stemmed from adopting the object-centered coordinate in their model designs, in which the reconstructed 3D shapes of the same class are all aligned to the same canonical pose regardless of different view-angles in the 2D observations. To this end, we propose an end-to-end framework to reconstruct the 3D mesh from a single image, where the reconstructed mesh is not only view-centered (i.e. its 3D pose respects the viewpoint of the 2D observation) but also preliminarily represented as a composition of volumetric 3D primitives before being further deformed into the fine-grained mesh to capture the shape details. In particular, the usage of volumetric primitives is motivated from the assumption that there generally exists some similar shape parts shared across various object categories, learning to estimate the primitive-based 3D model thus becomes more generalizable to the unseen categories. Furthermore, we advance to propose a novel mesh augmentation strategy, CvxRearrangement, to enrich the distribution of training shapes, which contributes to increasing the robustness of our proposed model and achieves better generalization. Extensive experiments demonstrate that our proposed method provides superior performance on both unseen and seen classes in comparison to several representative baselines of single-view 3D reconstruction.","PeriodicalId":431373,"journal":{"name":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123667114","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

Driving Anomaly Detection Using Contrastive Multiview Coding to Interpret Cause of Anomaly 基于对比多视图编码的驱动异常检测异常原因解释

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2022-10-23 DOI: 10.1109/IROS47612.2022.9981815

Yuning Qiu, Teruhisa Misu, C. Busso

{"title":"Driving Anomaly Detection Using Contrastive Multiview Coding to Interpret Cause of Anomaly","authors":"Yuning Qiu, Teruhisa Misu, C. Busso","doi":"10.1109/IROS47612.2022.9981815","DOIUrl":"https://doi.org/10.1109/IROS47612.2022.9981815","url":null,"abstract":"Modern advanced driver assistant systems (ADAS) rely on various types of sensors to monitor the vehicle status, driver's behaviors and road condition. The multimodal systems in the vehicle include sensors, such as accelerometers, pressure sensors, cameras, lidar and radars. When looking at a given scene with multiple modalities, there should be congruent in-formation among different modalities. Exploring the congruent information across modalities can lead to appealing solutions to create robust multimodal representations. This work proposes an unsupervised approach based on contrastive multiview coding (CMC) to capture the correlations in representations extracted from different modalities, learning a more discriminative rep-resentation space for unsupervised anomaly driving detection. We use CMC to train our model to extract view-invariant factors by maximizing the mutual information between mul-tiple representations from a given view, and increasing the distance of views from unrelated segments. We consider the vehicle driving data, driver's physiological data, and external environment data consisting of distances to nearby pedestrians, bicycles, and vehicles. The experimental results on the driving anomaly dataset (DAD) indicate that the CMC representation is effective for driving anomaly detection. The approach is efficient, scalable and interpretable, where the distances in the contrastive embedding for each view can be used to understand potential causes of the detected anomalies.","PeriodicalId":431373,"journal":{"name":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123721585","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

Manual Maneuverability: Metrics for Analysing and Benchmarking Kinesthetic Robot Guidance 手动操纵性:动觉机器人导向分析和基准的度量

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2022-10-23 DOI: 10.1109/IROS47612.2022.9981864

R. J. Kirschner, Florian Martineau, Nico Mansfeld, Saeed Abdolshah, S. Haddadin

{"title":"Manual Maneuverability: Metrics for Analysing and Benchmarking Kinesthetic Robot Guidance","authors":"R. J. Kirschner, Florian Martineau, Nico Mansfeld, Saeed Abdolshah, S. Haddadin","doi":"10.1109/IROS47612.2022.9981864","DOIUrl":"https://doi.org/10.1109/IROS47612.2022.9981864","url":null,"abstract":"Kinesthetic teaching of collaborative robots is applied for intuitive and flexible robot programming by demonstration. This enables non-experts to program such robots on the task-level. Multiple strategies exist to teach velocity- or torque-controlled robots and, thus, the maneuverability among commercial robots differs significantly. However, currently there exists no metric that quantifies how “well” the robot can be guided, e.g., how much effort is required to initiate a motion. In this paper, we propose standardized procedures to quantitatively assess robot manual maneuverability. First, we identify different motion phases during kinesthetic teaching. For each phase, we then propose metrics and experimental setups to evaluate them. The experimental protocols are applied to the proprietary teaching schemes of five commercial robots, namely the KUKA LWR iiwa 14, Yuanda Yu+, Franka Emika robot, and Universal Robot's UR5e and UR10e. The experimental comparison highlights distinct differences between the robots and shows that the proposed methods are a meaningful contribution to the performance and ergonomics assessment of collaborative robots.","PeriodicalId":431373,"journal":{"name":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125326352","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

Consensus-based Normalizing-Flow Control: A Case Study in Learning Dual-Arm Coordination 基于共识的归一化流控制:学习双臂协调的案例研究

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2022-10-23 DOI: 10.1109/IROS47612.2022.9981827

Hang Yin, Christos K. Verginis, D. Kragic

引用次数: 0

Grasp Planning for Occluded Objects in a Confined Space with Lateral View Using Monte Carlo Tree Search 基于蒙特卡罗树搜索的受限空间遮挡物横向抓取规划

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2022-10-23 DOI: 10.1109/IROS47612.2022.9981069

Minjae Kang, Hogun Kee, Junseok Kim, Songhwai Oh

{"title":"Grasp Planning for Occluded Objects in a Confined Space with Lateral View Using Monte Carlo Tree Search","authors":"Minjae Kang, Hogun Kee, Junseok Kim, Songhwai Oh","doi":"10.1109/IROS47612.2022.9981069","DOIUrl":"https://doi.org/10.1109/IROS47612.2022.9981069","url":null,"abstract":"In the lateral access environment, the robot be-havior should be planned considering surrounding objects and obstacles because object observation directions and approach angles are limited. To safely retrieve a partially occluded target object in these environments, we have to relocate objects using prehensile actions to create a collision-free path for the target. We propose a learning-based method for object rearrangement planning applicable to objects of various types and sizes in the lateral environment. We plan the optimal rearrangement sequence by considering both collisions and approach angles at which objects can be grasped. The proposed method finds the grasping order through Monte Carlo tree search, significantly reducing the tree search cost using point cloud states. In the experiment, the proposed method shows the best and most stable performance in various scenarios compared to the existing TAMP methods. In addition, we confirm that the proposed method trained in simulation can be easily applied to a real robot without additional fine-tuning, showing the robustness of the proposed method.","PeriodicalId":431373,"journal":{"name":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125534545","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: 1

ReINView: Re-interpreting Views for Multi-view 3D Object Recognition ReINView:重新解释多视图3D对象识别的视图

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2022-10-23 DOI: 10.1109/IROS47612.2022.9981777

Ruchang Xu, Wei Ma, Qing Mi, H. Zha

{"title":"ReINView: Re-interpreting Views for Multi-view 3D Object Recognition","authors":"Ruchang Xu, Wei Ma, Qing Mi, H. Zha","doi":"10.1109/IROS47612.2022.9981777","DOIUrl":"https://doi.org/10.1109/IROS47612.2022.9981777","url":null,"abstract":"Multi-view-based 3D object recognition is important in robot-environment interaction. However, recent methods simply extract features from each view via convolutional neural networks (CNNs) and then fuse these features together to make predictions. These methods ignore the inherent ambiguities of each view caused due to 3D-2D projection. To address this problem, we propose a novel deep framework for multi-view-based 3D object recognition. Instead of fusing the multi-view features directly, we design a re-interpretation module (ReINView) to eliminate the ambiguities at each view. To achieve this, ReINView re-interprets view features patch by patch by using their context from nearby views, considering that local patches are generally co-visible at nearby viewpoints. Since contour shapes are essential for 3D object recognition as well, ReINView further performs view-level re-interpretation, in which we use all the views as context sources since the target contours to be re-interpreted are globally observable. The re-interpreted multi-view features can better reflect the 3D global and local structures of the object. Experiments on both ModelNet40 and ModelNet10 show that the proposed model outperforms state-of-the-art methods in 3D object recognition.","PeriodicalId":431373,"journal":{"name":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115548659","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: 1

Maintaining Robot Localizability with Bayesian Cramér-Rao Lower Bounds 用贝叶斯cram<s:1> - rao下界保持机器人的可定位性

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2022-10-23 DOI: 10.1109/IROS47612.2022.9981427

Justin Cano, C. Chauffaut, É. Chaumette, Gael Pages, J. L. Ny

{"title":"Maintaining Robot Localizability with Bayesian Cramér-Rao Lower Bounds","authors":"Justin Cano, C. Chauffaut, É. Chaumette, Gael Pages, J. L. Ny","doi":"10.1109/IROS47612.2022.9981427","DOIUrl":"https://doi.org/10.1109/IROS47612.2022.9981427","url":null,"abstract":"Accurate and real-time position estimates are cru-cial for mobile robots. This work focuses on ranging-based positioning systems, which rely on distance measurements between known points, called anchors, and a tag to localize. The topology of the network formed by the anchors strongly influences the tag's localizability, i.e., its ability to be accurately localized. Here, the tag and some anchors are supposed to be carried by robots, which allows enhancing the positioning accuracy by planning the anchors' motions. We leverage Bayesian Cramer-Rao Lower Bounds (CRLBs) on the estimates' covariance in order to quantify the tag's localizability. This class of CRLBs can capture prior information on the tag's position and take it into account when deploying the anchors. We propose a method to decrease a potential function based on the Bayesian CRLB in order to maintain the localizability of the tag while having some prior knowledge about its position distribution. Then, we present a new experiment highlighting the link between the localizability potential and the precision expected in practice. Finally, two real-time anchor motion planners are demonstrated with ranging measurements in the presence or absence of prior information about the tag's position.","PeriodicalId":431373,"journal":{"name":"2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"206 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116386514","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