2020 IEEE International Conference on Robotics and Automation (ICRA)最新文献_第10页

Environment Prediction from Sparse Samples for Robotic Information Gathering 基于稀疏样本的机器人信息采集环境预测

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197263

Jeffrey A. Caley, Geoffrey A. Hollinger

引用次数: 5

A Dynamical System Approach for Adaptive Grasping, Navigation and Co-Manipulation with Humanoid Robots 类人机器人自适应抓取、导航与协同操作的动态系统方法

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197038

Nadia Figueroa, Salman Faraji, M. Koptev, A. Billard

{"title":"A Dynamical System Approach for Adaptive Grasping, Navigation and Co-Manipulation with Humanoid Robots","authors":"Nadia Figueroa, Salman Faraji, M. Koptev, A. Billard","doi":"10.1109/ICRA40945.2020.9197038","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197038","url":null,"abstract":"In this paper, we present an integrated approach that provides compliant control of an iCub humanoid robot and adaptive reaching, grasping, navigating and co-manipulating capabilities. We use state-dependent dynamical systems (DS) to (i) coordinate and drive the robots hands (in both position and orientation) to grasp an object using an intermediate virtual object, and (ii) drive the robot's base while walking/navigating. The use of DS as motion generators allows us to adapt smoothly as the object moves and to re-plan on-line motion of the arms and body to reach the object's new location. The desired motion generated by the DS are used in combination with a whole-body compliant control strategy that absorbs perturbations while walking and offers compliant behaviors for grasping and manipulation tasks. Further, the desired dynamics for the arm and body can be learned from demonstrations. By integrating these components, we achieve unprecedented adaptive behaviors for whole body manipulation. We showcase this in simulations and real-world experiments where iCub robots (i) walk-to-grasp objects, (ii) follow a human (or another iCub) through interaction and (iii) learn to navigate or comanipulate an object from human guided demonstrations; whilst being robust to changing targets and perturbations.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"53 1","pages":"7676-7682"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84787223","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

Cooperative Multi-Robot Navigation in Dynamic Environment with Deep Reinforcement Learning 基于深度强化学习的动态环境下多机器人协同导航

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197209

Ruihua Han, Shengduo Chen, Qi Hao

{"title":"Cooperative Multi-Robot Navigation in Dynamic Environment with Deep Reinforcement Learning","authors":"Ruihua Han, Shengduo Chen, Qi Hao","doi":"10.1109/ICRA40945.2020.9197209","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197209","url":null,"abstract":"The challenges of multi-robot navigation in dynamic environments lie in uncertainties in obstacle complexities, partially observation of robots, and policy implementation from simulations to the real world. This paper presents a cooperative approach to address the multi-robot navigation problem (MRNP) under dynamic environments using a deep reinforcement learning (DRL) framework, which can help multiple robots jointly achieve optimal paths despite a certain degree of obstacle complexities. The novelty of this work includes threefold: (1) developing a cooperative architecture that robots can exchange information with each other to select the optimal target locations; (2) developing a DRL based framework which can learn a navigation policy to generate the optimal paths for multiple robots; (3) developing a training mechanism based on dynamics randomization which can make the policy generalized and achieve the maximum performance in the real world. The method is tested with Gazebo simulations and 4 differential drive robots. Both simulation and experiment results validate the superior performance of the proposed method in terms of success rate and travel time when compared with the other state-of-art technologies.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"21 1","pages":"448-454"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84804878","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: 21

Mechanically Programmed Miniature Origami Grippers 机械编程微型折纸手

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196545

Alec Orlofsky, Chang Liu, S. Kamrava, A. Vaziri, Samuel M. Felton

引用次数: 6

Error estimation and correction in a spiking neural network for map formation in neuromorphic hardware 神经形态硬件中地图形成的尖峰神经网络误差估计与校正

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197498

Raphaela Kreiser, Gabriel Waibel, Nuria Armengol, Alpha Renner, Yulia Sandamirskaya

引用次数: 13

Dynamic Coupling as an Indicator of Gait Robustness for Underactuated Biped Robots 欠驱动双足机器人步态鲁棒性的动态耦合指标

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197203

Martin Fevre, J. Schmiedeler

引用次数: 4

SPRINT: Subgraph Place Recognition for INtelligent Transportation SPRINT:智能交通的子图位置识别

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196522

Y. Latif, Anh-Dzung Doan, Tat-Jun Chin, I. Reid

引用次数: 3

Feedback Linearization for Uncertain Systems via Reinforcement Learning 基于强化学习的不确定系统反馈线性化

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197158

T. Westenbroek, David Fridovich-Keil, Eric V. Mazumdar, Shreyas Arora, Valmik Prabhu, S. Sastry, C. Tomlin

{"title":"Feedback Linearization for Uncertain Systems via Reinforcement Learning","authors":"T. Westenbroek, David Fridovich-Keil, Eric V. Mazumdar, Shreyas Arora, Valmik Prabhu, S. Sastry, C. Tomlin","doi":"10.1109/ICRA40945.2020.9197158","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197158","url":null,"abstract":"We present a novel approach to control design for nonlinear systems which leverages model-free policy optimization techniques to learn a linearizing controller for a physical plant with unknown dynamics. Feedback linearization is a technique from nonlinear control which renders the input-output dynamics of a nonlinear plant linear under application of an appropriate feedback controller. Once a linearizing controller has been constructed, desired output trajectories for the nonlinear plant can be tracked using a variety of linear control techniques. However, the calculation of a linearizing controller requires a precise dynamics model for the system. As a result, model-based approaches for learning exact linearizing controllers generally require a simple, highly structured model of the system with easily identifiable parameters. In contrast, the model-free approach presented in this paper is able to approximate the linearizing controller for the plant using general function approximation architectures. Specifically, we formulate a continuous-time optimization problem over the parameters of a learned linearizing controller whose optima are the set of parameters which best linearize the plant. We derive conditions under which the learning problem is (strongly) convex and provide guarantees which ensure the true linearizing controller for the plant is recovered. We then discuss how model-free policy optimization algorithms can be used to solve a discrete-time approximation to the problem using data collected from the real-world plant. The utility of the framework is demonstrated in simulation and on a real-world robotic platform.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"14 1","pages":"1364-1371"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85143310","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: 17

Kinematic Modeling and Compliance Modulation of Redundant Manipulators Under Bracing Constraints 支撑约束下冗余机械手的运动学建模与柔度调制

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9197387

Garrison L. H. Johnston, A. Orekhov, N. Simaan

{"title":"Kinematic Modeling and Compliance Modulation of Redundant Manipulators Under Bracing Constraints","authors":"Garrison L. H. Johnston, A. Orekhov, N. Simaan","doi":"10.1109/ICRA40945.2020.9197387","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197387","url":null,"abstract":"Collaborative robots should ideally use low torque actuators for passive safety reasons. However, some applications require these collaborative robots to reach deep into confined spaces while assisting a human operator in physically demanding tasks. In this paper, we consider the use of in-situ collaborative robots (ISCRs) that balance the conflicting demands of passive safety dictating low torque actuation and the need to reach into deep confined spaces. We consider the judicious use of bracing as a possible solution to these conflicting demands and present a modeling framework that takes into account the constrained kinematics and the effect of bracing on the endeffector compliance. We then define a redundancy resolution framework that minimizes the directional compliance of the end-effector while maximizing end-effector dexterity. Kinematic simulation results show that the redundancy resolution strategy successfully decreases compliance and improves kinematic conditioning while satisfying the constraints imposed by the bracing task. Applications of this modeling framework can support future research on the choice of bracing locations and support the formation of an admittance control framework for collaborative control of ISCRs under bracing constraints. Such robots can benefit workers in the future by reducing the physiological burdens that contribute to musculoskeletal injury.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":"75 1","pages":"4709-4716"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84011206","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

SwarmRail: A Novel Overhead Robot System for Indoor Transport and Mobile Manipulation 用于室内运输和移动操作的新型架空机器人系统

2020 IEEE International Conference on Robotics and Automation (ICRA) Pub Date : 2020-05-01 DOI: 10.1109/ICRA40945.2020.9196972

M. Görner, Fabian Benedikt, Ferdinand Grimmel, T. Hulin

引用次数: 2