2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)最新文献_第2页

Closed-loop Force Control of a Pneumatic Gripper Actuated by Two Pressure Regulators 双压力调节器驱动气动夹持器的闭环力控制

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8968226

R. A. Romeo, Luca Fiorio, Edwin Johnatan Avila Mireles, F. Cannella, G. Metta, D. Pucci

{"title":"Closed-loop Force Control of a Pneumatic Gripper Actuated by Two Pressure Regulators","authors":"R. A. Romeo, Luca Fiorio, Edwin Johnatan Avila Mireles, F. Cannella, G. Metta, D. Pucci","doi":"10.1109/IROS40897.2019.8968226","DOIUrl":"https://doi.org/10.1109/IROS40897.2019.8968226","url":null,"abstract":"Robotic arms can perform grasping actions thanks to their “dexteorus” part, i.e. the gripper. Among the various categories, nowadays pneumatic grippers became the most employed in industry, as they have low cost and little bulkiness. Despite their simplicity, controlling the force applied by these grippers is not straightforward due to the dependence of such a force on the air pressure in the gripper chambers. As a result, it is still tricky to implement closed-loop force control for pneumatic grippers. This paper intends to deliver a control scheme relying on the force measurement to control pneumatic grippers. The force might be measured through a commercial sensor (e.g. a load cell) and fed back to close the control loop. This includes a calibration which maps the force-pressure relation taking into account both desired force and length of the gripper fingers. The control scheme exploits two different pressure regulators to precisely adjust the air pressure inside the gripper chambers (i.e. opening and closing chambers). To this aim, a quadratic programming algorithm is employed. The control scheme performance revealed to be good: results will be shown in terms of gripper response to sinusoidal and step inputs, along with the pressure-force characterization.","PeriodicalId":428768,"journal":{"name":"2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125396923","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: 4

Introducing a Scalable and Modular Control Framework for Low-cost Monocular Robots in Hazardous Environments 危险环境下低成本单目机器人的可扩展模块化控制框架

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8967521

Hazel M. Taylor, C. Dondrup, K. Lohan

引用次数: 4

Experimental Validation of Hydraulic Interlocking Drive System for Biped Humanoid Robot 双足仿人机器人液压联锁驱动系统的实验验证

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8968253

J. Shimizu, T. Otani, H. Mizukami, K. Hashimoto, A. Takanishi

{"title":"Experimental Validation of Hydraulic Interlocking Drive System for Biped Humanoid Robot","authors":"J. Shimizu, T. Otani, H. Mizukami, K. Hashimoto, A. Takanishi","doi":"10.1109/IROS40897.2019.8968253","DOIUrl":"https://doi.org/10.1109/IROS40897.2019.8968253","url":null,"abstract":"Biped robots require substantial amounts of power on each leg alternately while walking, hopping, and running. However, it is difficult to adopt large high-power electrical motors in conventional mechanical transmission systems owing to spatial limitations. To address this problem, a hydraulic interlocking drive system that incorporates two hydraulic direct-drive systems is proposed for biped humanoid robots. The hydraulic interlocking drive system connects the two hydraulic direct-drive systems and concentrates the pump output on one side cylinder. The other side cylinder meter-in flow rate is controlled by the meter-out flow rate from the cylinder on which the pump is concentrated. Good position tracking and excellent energy saving are achieved with the proposed system. A performance comparison with a single hydraulic direct-drive system shows that the motor power of the hip pitch joint is reduced by 27.3% for walking patterns. This result shows that the rated output of the motor can be reduced, and smaller and lighter motors can be installed in biped robots.","PeriodicalId":428768,"journal":{"name":"2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122345551","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

COBRA: COllaborative Bot with multi-Rotor Actuation COBRA:多转子驱动的协作机器人

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8968480

Camilo Ordonez, O. Chuy, Tomas Fajardo

引用次数: 1

ResFlow: Multi-tasking of Sequentially Pooling Spatiotemporal Features for Action Recognition and Optical Flow Estimation 基于时序池化时空特征的多任务动作识别和光流估计

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8968533

Tso-Hsin Yeh, Chuan Kuo, An-Sheng Liu, Yu-Hung Liu, Yu-Huan Yang, Zi-Jun Li, J. Shen, L. Fu

{"title":"ResFlow: Multi-tasking of Sequentially Pooling Spatiotemporal Features for Action Recognition and Optical Flow Estimation","authors":"Tso-Hsin Yeh, Chuan Kuo, An-Sheng Liu, Yu-Hung Liu, Yu-Huan Yang, Zi-Jun Li, J. Shen, L. Fu","doi":"10.1109/IROS40897.2019.8968533","DOIUrl":"https://doi.org/10.1109/IROS40897.2019.8968533","url":null,"abstract":"Since deep-learning-based method has been widely-used and is capable of generating generic model, most existing methods about action recognition use either two-stream structure, considering spatial and temporal features separately, or C3D, costing lots of prices in memory and time. We aim to design a robust system to extract spatiotemporal features with aggregation mechanism to integrate local features in temporal order. In light of this, we propose ResFlow to estimate optical flow and predict action recognition simultaneously. Leveraging the characteristic of optical flow estimation, we extract spatiotemporal feature via an autoencoder. Via a novel Sequentially Pooling Mechanism which literally pool global spatiotemporal feature sequentially, we extract spatiotemporal feature at each time and aggregate these local features into global feature. This design use only RGB images as input with temporal information encoded, pre-trained by optical flow, and sequentially aggregate spatiotemporal features in high efficiency. We evaluate our ability of estimating optical flow on FlyingChairs dataset and show the promising results of action recognition on UCF-101 dataset through a series of experiments.","PeriodicalId":428768,"journal":{"name":"2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122892742","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 and Accurate Algorithm for the Perspecitve-n-Point Problem 一种高效、精确的透视n点问题算法

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8968482

Lipu Zhou, M. Kaess

{"title":"An Efficient and Accurate Algorithm for the Perspecitve-n-Point Problem","authors":"Lipu Zhou, M. Kaess","doi":"10.1109/IROS40897.2019.8968482","DOIUrl":"https://doi.org/10.1109/IROS40897.2019.8968482","url":null,"abstract":"In this paper, we address the problem of pose estimation from N 2D/3D point correspondences, known as the Perspective-n-Point (PnP) problem. Although many solutions have been proposed, it is hard to optimize both computational complexity and accuracy at the same time. In this paper, we propose an accurate and simultaneously efficient solution to the PnP problem. Previous PnP algorithms generally involve two sets of unknowns including the depth of each pixel and the pose of the camera. Our formulation does not involve the depth of each pixel. By introducing some intermediate variables, this formulation leads to a fourth degree polynomial cost function with 3 unknowns that only involves the rotation. In contrast to previous works, we do not address this minimization problem by solving the first-order optimality conditions using the off-the-shelf Gröbner basis method, as the Gröbner basis method may encounter numeric problems. Instead, we present a method based on linear system null space analysis to provide a robust initial estimation for a Newton iteration. Experimental results demonstrate that our algorithm is comparable to the start-of-the-art algorithms in terms of accuracy, and the speed of our algorithm is among the fastest algorithms.","PeriodicalId":428768,"journal":{"name":"2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122986899","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: 15

Fast and Safe Policy Adaptation via Alignment-based Transfer 通过基于对齐的转移实现快速安全的策略适应

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8967966

Jigang Kim, Seungwon Choi, H. Kim

{"title":"Fast and Safe Policy Adaptation via Alignment-based Transfer","authors":"Jigang Kim, Seungwon Choi, H. Kim","doi":"10.1109/IROS40897.2019.8967966","DOIUrl":"https://doi.org/10.1109/IROS40897.2019.8967966","url":null,"abstract":"Applying deep reinforcement learning to physical systems, as opposed to learning in simulation, presents additional challenges in terms of sample efficiency and safety. Collecting large amounts of hardware demonstration data is time-consuming and the exploratory behavior of reinforcement learning algorithms may lead the system into dangerous states, especially during the early stages of training. To address these challenges, we apply transfer learning to reuse a previously learned policy instead of learning from scratch. In this paper, we propose a method where given a source policy, policy adaptation is performed via transfer learning to produce a target policy suitable for real-world deployment. For policy adaptation, alignment-based transfer learning is applied to trajectories generated by the source policy and their corresponding safe target trajectories. We apply this method to manipulators and show that the proposed method is applicable to both inter-task and inter-robot transfer whilst considering safety. We also show that the resulting target policy is robust and can be further improved with reinforcement learning.","PeriodicalId":428768,"journal":{"name":"2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114176708","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

Inferring Distributions Over Depth from a Single Image 从单幅图像推断深度分布

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8968065

Gengshan Yang, Peiyun Hu, Deva Ramanan

{"title":"Inferring Distributions Over Depth from a Single Image","authors":"Gengshan Yang, Peiyun Hu, Deva Ramanan","doi":"10.1109/IROS40897.2019.8968065","DOIUrl":"https://doi.org/10.1109/IROS40897.2019.8968065","url":null,"abstract":"When building a geometric scene understanding system for autonomous vehicles, it is crucial to know when the system might fail. Most contemporary approaches cast the problem as depth regression, whose output is a depth value for each pixel. Such approaches cannot diagnose when failures might occur. One attractive alternative is a deep Bayesian network, which captures uncertainty in both model parameters and ambiguous sensor measurements. However, estimating uncertainties is often slow and the distributions are often limited to be uni-modal. In this paper, we recast the continuous problem of depth regression as discrete binary classification, whose output is an un-normalized distribution over possible depths for each pixel. Such output allows one to reliably and efficiently capture multi-modal depth distributions in ambiguous cases, such as depth discontinuities and reflective surfaces. Results on standard benchmarks show that our method produces accurate depth predictions and significantly better uncertainty estimations than prior art while running near real-time. Finally, by making use of uncertainties of the predicted distribution, we significantly reduce streak-like artifacts and improves accuracy as well as memory efficiency in 3D map reconstruction. Video and code can be found on the project website1.","PeriodicalId":428768,"journal":{"name":"2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122009622","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: 19

Cooperative Range-only SLAM based on Sum of Gaussian Filter in Dynamic Environments 动态环境下基于高斯滤波和的协同距离SLAM

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8967646

Jung-Hee Kim, Doik Kim

引用次数: 8

DISR: Deep Infrared Spectral Restoration Algorithm for Robot Sensing and Intelligent Visual Tracking Systems 机器人传感和智能视觉跟踪系统的深红外光谱恢复算法

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) Pub Date : 2019-11-01 DOI: 10.1109/IROS40897.2019.8967891

Hai Liu, Youfu Li, Dan Su, Zhaoli Zhang, Sannyuya Liu, Tingting Liu

引用次数: 10