2016 IEEE International Conference on Robotics and Automation (ICRA)最新文献_第5页

Differential jumping: A novel mode for micro-robot navigation 微分跳跃:微型机器人导航的一种新模式

2016 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2016-05-16 DOI: 10.1109/ICRA.2016.7487570

T. Bandyopadhyay, Karl von Richter, Marc-Antoine Pallaud, A. Elfes

引用次数: 5

An integrated approach to visual perception of articulated objects 一个综合的方法来视觉感知铰接的对象

2016 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2016-05-16 DOI: 10.1109/ICRA.2016.7487714

R. M. Martin, S. Höfer, O. Brock

引用次数: 45

Differential feed control applied to corner matching in automated sewing 差速进给控制应用于自动缝角匹配

2016 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2016-05-16 DOI: 10.1109/ICRA.2016.7487578

J. Schrimpf, G. Mathisen

引用次数: 1

Performance without tweaking differentiators via a PR controller: Furuta pendulum case study 无需通过PR控制器调整微分器的性能:古古摆案例研究

2016 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2016-05-16 DOI: 10.1109/ICRA.2016.7487566

Teresa Ortega, R. Villafuerte, Carlos Vázquez, L. Freidovich

引用次数: 7

Sensorless and constraint based peg-in-hole task execution with a dual-arm robot 基于无传感器和约束的双臂机器人钉孔任务执行

2016 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2016-05-16 DOI: 10.1109/ICRA.2016.7487161

Matteo Parigi Polverini, A. Zanchettin, S. Castello, P. Rocco

引用次数: 38

Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards Dex-Net 1.0:基于云的3D物体网络，使用具有相关奖励的多臂强盗模型进行鲁棒抓取规划

2016 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2016-05-16 DOI: 10.1109/ICRA.2016.7487342

Jeffrey Mahler, Florian T. Pokorny, Brian Hou, Melrose Roderick, Michael Laskey, Mathieu Aubry, Kai J. Kohlhoff, T. Kröger, J. Kuffner, Ken Goldberg

{"title":"Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards","authors":"Jeffrey Mahler, Florian T. Pokorny, Brian Hou, Melrose Roderick, Michael Laskey, Mathieu Aubry, Kai J. Kohlhoff, T. Kröger, J. Kuffner, Ken Goldberg","doi":"10.1109/ICRA.2016.7487342","DOIUrl":"https://doi.org/10.1109/ICRA.2016.7487342","url":null,"abstract":"This paper presents the Dexterity Network (Dex-Net) 1.0, a dataset of 3D object models and a sampling-based planning algorithm to explore how Cloud Robotics can be used for robust grasp planning. The algorithm uses a Multi- Armed Bandit model with correlated rewards to leverage prior grasps and 3D object models in a growing dataset that currently includes over 10,000 unique 3D object models and 2.5 million parallel-jaw grasps. Each grasp includes an estimate of the probability of force closure under uncertainty in object and gripper pose and friction. Dex-Net 1.0 uses Multi-View Convolutional Neural Networks (MV-CNNs), a new deep learning method for 3D object classification, to provide a similarity metric between objects, and the Google Cloud Platform to simultaneously run up to 1,500 virtual cores, reducing experiment runtime by up to three orders of magnitude. Experiments suggest that correlated bandit techniques can use a cloud-based network of object models to significantly reduce the number of samples required for robust grasp planning. We report on system sensitivity to variations in similarity metrics and in uncertainty in pose and friction. Code and updated information is available at http://berkeleyautomation.github.io/dex-net/.","PeriodicalId":200117,"journal":{"name":"2016 IEEE International Conference on Robotics and Automation (ICRA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130087957","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: 321

Hierarchical semantic parsing for object pose estimation in densely cluttered scenes 密集杂乱场景中目标姿态估计的层次语义分析

2016 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2016-05-16 DOI: 10.1109/ICRA.2016.7487712

Chi Li, J. Bohren, Eric Carlson, Gregory Hager

{"title":"Hierarchical semantic parsing for object pose estimation in densely cluttered scenes","authors":"Chi Li, J. Bohren, Eric Carlson, Gregory Hager","doi":"10.1109/ICRA.2016.7487712","DOIUrl":"https://doi.org/10.1109/ICRA.2016.7487712","url":null,"abstract":"Densely cluttered scenes are composed of multiple objects which are in close contact and heavily occlude each other. Few existing 3D object recognition systems are capable of accurately predicting object poses in such scenarios. This is mainly due to the presence of objects with textureless surfaces, similar appearances and the difficulty of object instance segmentation. In this paper, we present a hierarchical semantic segmentation algorithm which partitions a densely cluttered scene into different object regions. A RANSAC-based registration method is subsequently applied to estimate 6-DoF object poses within each object class. Part of this algorithm includes a generalized pooling scheme used to construct robust and discriminative object representations from a convolutional architecture with multiple pooling domains. We also provide a new RGB-D dataset which serves as a benchmark for object pose estimation in densely cluttered scenes. This dataset contains five thousand scene frames and over twenty thousand labeled poses of ten common hand tools. We show that our method demonstrates improved performance of pose estimation on this new dataset compared with other state-of-the-art methods.","PeriodicalId":200117,"journal":{"name":"2016 IEEE International Conference on Robotics and Automation (ICRA)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134154016","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: 18

A task-driven algorithm for configuration synthesis of modular robots 模块化机器人组态综合的任务驱动算法

2016 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2016-05-16 DOI: 10.1109/ICRA.2016.7487727

Esra Icer, Andrea Giusti, M. Althoff

引用次数: 12

Bat Bot (B2), a biologically inspired flying machine 蝙蝠机器人(B2)，一种受生物启发的飞行器

2016 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2016-05-16 DOI: 10.1109/ICRA.2016.7487491

A. Ramezani, Xichen Shi, Soon-Jo Chung, S. Hutchinson

引用次数: 68

Distributed sensing and nonlinear MISO models for predicting the propulsive forces of flexible, multi-DOF robotic fins 柔性多自由度机器人鳍推进力预测的分布式传感和非线性MISO模型

2016 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2016-05-16 DOI: 10.1109/ICRA.2016.7487674

Jeff C. Kahn, J. Tangorra

{"title":"Distributed sensing and nonlinear MISO models for predicting the propulsive forces of flexible, multi-DOF robotic fins","authors":"Jeff C. Kahn, J. Tangorra","doi":"10.1109/ICRA.2016.7487674","DOIUrl":"https://doi.org/10.1109/ICRA.2016.7487674","url":null,"abstract":"Fish are capable of producing a wide repertoire of 3D propulsive forces using their fins, and have inspired the development of compliant, multiple-DOF, robotic fins with similar capabilities. Most of these robotic fins are under open-loop control on propulsive force because the forces are challenging to model. Understanding how to predict propulsive forces for these types of fins would significantly advance the state of the art towards closed-loop control of forces. Distributed sensors within robotic fins have been used to predict propulsive forces using linear models, but these models fail to predict forces when fin kinematics become more complex. The objective of the work presented herein is to understand the use of nonlinear, multiple-input-single-output (MISO) Volterra series models between intrinsic sensory measurements and propulsive forces of a flexible robotic fin. Techniques in nonlinear system identification are used to address model conditioning. Nonlinear models predict the propulsive forces well, capturing features of both thrust and lateral forces. Nonlinear models significantly outperformed linear models both in cost of implementation and performance. The best sensor sampling practice was to sample from multiple locations with both pressure and bending modalities. Distributed sensing paired with nonlinear Volterra series models was successful for predicting the forces created by flexible robotic fins with complex kinematics and multiple degrees of freedom.","PeriodicalId":200117,"journal":{"name":"2016 IEEE International Conference on Robotics and Automation (ICRA)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131827236","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