2020 IEEE International Conference on Robotics and Automation (ICRA)最新文献

CNN-Based Simultaneous Dehazing and Depth Estimation 基于cnn的同步除雾和深度估计

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197358

Byeong-uk Lee, Kyunghyun Lee, Jean Oh, I. Kweon

{"title":"CNN-Based Simultaneous Dehazing and Depth Estimation","authors":"Byeong-uk Lee, Kyunghyun Lee, Jean Oh, I. Kweon","doi":"10.1109/ICRA40945.2020.9197358","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197358","url":null,"abstract":"It is difficult for both cameras and depth sensors to obtain reliable information in hazy scenes. Therefore, image dehazing is still one of the most challenging problems to solve in computer vision and robotics. With the development of convolutional neural networks (CNNs), lots of dehazing and depth estimation algorithms using CNNs have emerged. However, very few of those try to solve these two problems at the same time. Focusing on the fact that traditional haze modeling contains depth information in its formula, we propose a CNN-based simultaneous dehazing and depth estimation network. Our network aims to estimate both a dehazed image and a fully scaled depth map from a single hazy RGB input with end-toend training. The network contains a single dense encoder and four separate decoders; each of them shares the encoded image representation while performing individual tasks. We suggest a novel depth-transmission consistency loss in the training scheme to fully utilize the correlation between the depth information and transmission map. To demonstrate the robustness and effectiveness of our algorithm, we performed various ablation studies and compared our results to those of state-of-the-art algorithms in dehazing and single image depth estimation, both qualitatively and quantitatively. Furthermore, we show the generality of our network by applying it to some real-world examples.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73484759","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: 12

Vision Aided Dynamic Exploration of Unstructured Terrain with a Small-Scale Quadruped Robot 小型四足机器人的视觉辅助非结构化地形动态探测

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196777

Donghyun Kim, D. Carballo, J. Carlo, Benjamin Katz, G. Bledt, Bryan Lim, Sangbae Kim

引用次数: 65

Probabilistic Effect Prediction through Semantic Augmentation and Physical Simulation 基于语义增强和物理模拟的概率效应预测

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197477

A. Bauer, Peter Schmaus, F. Stulp, Daniel Leidner

引用次数: 11

High Resolution Soft Tactile Interface for Physical Human-Robot Interaction 用于人机物理交互的高分辨率软触觉界面

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197365

Isabella Huang, R. Bajcsy

{"title":"High Resolution Soft Tactile Interface for Physical Human-Robot Interaction","authors":"Isabella Huang, R. Bajcsy","doi":"10.1109/ICRA40945.2020.9197365","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197365","url":null,"abstract":"If robots and humans are to coexist and cooperate in society, it would be useful for robots to be able to engage in tactile interactions. Touch is an intuitive communication tool as well as a fundamental method by which we assist each other physically. Tactile abilities are challenging to engineer in robots, since both mechanical safety and sensory intelligence are imperative. Existing work reveals a trade-off between these principles— tactile interfaces that are high in resolution are not easily adapted to human-sized geometries, nor are they generally compliant enough to guarantee safety. On the other hand, soft tactile interfaces deliver intrinsically safe mechanical properties, but their non-linear characteristics render them difficult for use in timely sensing and control. We propose a robotic system that is equipped with a completely soft and therefore safe tactile interface that is large enough to interact with human upper limbs, while producing high resolution tactile sensory readings via depth camera imaging of the soft interface. We present and validate a data-driven model that maps point cloud data to contact forces, and verify its efficacy by demonstrating two real-world applications. In particular, the robot is able to react to a human finger’s pokes and change its pose based on the tactile input. In addition, we also demonstrate that the robot can act as an assistive device that dynamically supports and follows a human forearm from underneath.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75821813","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: 6

A Novel Solar Tracker Driven by Waves: From Idea to Implementation* 一种由波浪驱动的新型太阳能跟踪器:从想法到实现*

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196998

Ruoyu Xu, Hengli Liu, Chongfeng Liu, Zhenglong Sun, Tin Lun Lam, Huihuan Qian

{"title":"A Novel Solar Tracker Driven by Waves: From Idea to Implementation*","authors":"Ruoyu Xu, Hengli Liu, Chongfeng Liu, Zhenglong Sun, Tin Lun Lam, Huihuan Qian","doi":"10.1109/ICRA40945.2020.9196998","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196998","url":null,"abstract":"Traditional solar trackers often adopt motors to automatically adjust the attitude of the solar panels towards the sun for maximum power efficiency. In this paper, a novel design of solar tracker for the ocean environment is introduced. Utilizing the fluctuations due to the waves, electromagnetic brakes are utilized instead of motors to adjust the attitude of the solar panels. Compared with the traditional solar trackers, the proposed one is simpler in hardware while the harvesting efficiency is similar. The desired attitude is calculated out of the local location and time. Then based on the dynamic model of the system, the angular acceleration of the solar panels is estimated and a control algorithm is proposed to decide the release and lock states of the brakes. In such a manner, the adjustment of the attitude of the solar panels can be achieved by using two brakes only. Experiments are conducted to validate the acceleration estimator and the dynamic model. At last, the feasibility of the proposed solar tracker is tested on the real water surface. The results show that the system is able to adjust 40° in two dimensions within 28 seconds.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74609495","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

Picking Thin Objects by Tilt-and-Pivot Manipulation and Its Application to Bin Picking 倾斜和枢轴操作拾取薄物体及其在拣箱中的应用

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197493

Zhekai Tong, Tierui He, Chung Hee Kim, Yu Hin Ng, Qianyi Xu, Jungwon Seo

引用次数: 6

Optimizing performance in automation through modular robots 通过模块化机器人优化自动化性能

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196590

Stefan B. Liu, M. Althoff

引用次数: 12

Finding Missing Skills for High-Level Behaviors 寻找高级行为缺失的技能

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197223

Adam Pacheck, Salar Moarref, H. Kress-Gazit

引用次数: 6

End-to-end Learning for Inter-Vehicle Distance and Relative Velocity Estimation in ADAS with a Monocular Camera 基于单目相机的ADAS车辆间距离和相对速度估计的端到端学习

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197557

Zhenbo Song, Jianfeng Lu, Tong Zhang, Hongdong Li

引用次数: 10

Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation 帮助机器人学习:通过虚拟现实远程操作演示的人机师徒模型

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196754

Joseph DelPreto, J. Lipton, Lindsay M. Sanneman, Aidan J. Fay, Christopher K. Fourie, Changhyun Choi, D. Rus

{"title":"Helping Robots Learn: A Human-Robot Master-Apprentice Model Using Demonstrations via Virtual Reality Teleoperation","authors":"Joseph DelPreto, J. Lipton, Lindsay M. Sanneman, Aidan J. Fay, Christopher K. Fourie, Changhyun Choi, D. Rus","doi":"10.1109/ICRA40945.2020.9196754","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196754","url":null,"abstract":"As artificial intelligence becomes an increasingly prevalent method of enhancing robotic capabilities, it is important to consider effective ways to train these learning pipelines and to leverage human expertise. Working towards these goals, a master-apprentice model is presented and is evaluated during a grasping task for effectiveness and human perception. The apprenticeship model augments self-supervised learning with learning by demonstration, efficiently using the human’s time and expertise while facilitating future scalability to supervision of multiple robots; the human provides demonstrations via virtual reality when the robot cannot complete the task autonomously. Experimental results indicate that the robot learns a grasping task with the apprenticeship model faster than with a solely self-supervised approach and with fewer human interventions than a solely demonstration-based approach; 100% grasping success is obtained after 150 grasps with 19 demonstrations. Preliminary user studies evaluating workload, usability, and effectiveness of the system yield promising results for system scalability and deployability. They also suggest a tendency for users to overestimate the robot’s skill and to generalize its capabilities, especially as learning improves.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76155789","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: 20