咨询与建议

限定检索结果

文献类型

  • 72 篇 期刊文献
  • 46 篇 会议

馆藏范围

  • 118 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 80 篇 工学
    • 58 篇 计算机科学与技术...
    • 56 篇 控制科学与工程
    • 56 篇 软件工程
    • 16 篇 生物工程
    • 13 篇 机械工程
    • 9 篇 生物医学工程(可授...
    • 8 篇 力学(可授工学、理...
    • 7 篇 仪器科学与技术
    • 6 篇 电气工程
    • 5 篇 化学工程与技术
    • 5 篇 交通运输工程
    • 4 篇 光学工程
    • 3 篇 信息与通信工程
    • 3 篇 建筑学
    • 3 篇 土木工程
    • 3 篇 安全科学与工程
    • 2 篇 材料科学与工程(可...
  • 46 篇 理学
    • 22 篇 数学
    • 15 篇 生物学
    • 14 篇 统计学(可授理学、...
    • 11 篇 物理学
    • 8 篇 系统科学
    • 6 篇 化学
  • 9 篇 管理学
    • 6 篇 管理科学与工程(可...
    • 3 篇 图书情报与档案管...
  • 6 篇 医学
    • 6 篇 基础医学(可授医学...
    • 6 篇 临床医学
    • 4 篇 药学(可授医学、理...
  • 4 篇 法学
    • 4 篇 社会学
  • 2 篇 教育学
    • 2 篇 教育学
  • 2 篇 农学

主题

  • 13 篇 motion planning
  • 8 篇 planning
  • 6 篇 trajectory
  • 5 篇 reinforcement le...
  • 5 篇 deep learning
  • 5 篇 robots
  • 4 篇 uncertainty
  • 4 篇 measurement
  • 3 篇 motion estimatio...
  • 3 篇 grasping
  • 3 篇 safety
  • 3 篇 optimization
  • 3 篇 robot sensing sy...
  • 2 篇 tools
  • 2 篇 conferences
  • 2 篇 programming
  • 2 篇 task analysis
  • 2 篇 three-dimensiona...
  • 2 篇 continuous time ...
  • 2 篇 buildings

机构

  • 23 篇 machine learning...
  • 8 篇 learning and int...
  • 7 篇 learning and int...
  • 7 篇 machine learning...
  • 6 篇 the learning sys...
  • 6 篇 max planck insti...
  • 5 篇 vector institute...
  • 4 篇 bosch center for...
  • 4 篇 technical univer...
  • 4 篇 the university o...
  • 4 篇 autonomous syste...
  • 4 篇 max planck insti...
  • 4 篇 the vector insti...
  • 4 篇 max planck insti...
  • 3 篇 abb corporate re...
  • 3 篇 university of ta...
  • 3 篇 max-planck insti...
  • 3 篇 max plank eth ce...
  • 3 篇 division of robo...
  • 2 篇 school of mathem...

作者

  • 27 篇 toussaint marc
  • 17 篇 schoellig angela...
  • 12 篇 mainprice jim
  • 12 篇 marc toussaint
  • 9 篇 zhou siqi
  • 7 篇 oguz ozgur s.
  • 7 篇 driess danny
  • 6 篇 angela p. schoel...
  • 6 篇 brunke lukas
  • 6 篇 kratzer philipp
  • 5 篇 siqi zhou
  • 5 篇 danny driess
  • 5 篇 hartmann valenti...
  • 4 篇 ozgur s. oguz
  • 4 篇 jim mainprice
  • 4 篇 schaal stefan
  • 4 篇 oh yoojin
  • 4 篇 bennewitz maren
  • 3 篇 pan sicong
  • 3 篇 heins adam

语言

  • 109 篇 英文
  • 8 篇 其他
  • 1 篇 中文
检索条件"机构=the Learning Systems and Robotics Lab"
118 条 记 录,以下是61-70 订阅
排序:
Non-invasive urinary bladder volume estimation with artefact-suppressed bio-impedance measurements
arXiv
收藏 引用
arXiv 2023年
作者: Dheman, Kanika Walser, Stefan Mayer, Philipp Eggimann, Manuel Kozomara, Marko Franke, Denise Hermanns, Thomas Sax, Hugo Schürle, Simone Magno, Michele Project Based Learning Center ETH Zürich Switzerland Multi-Scale Robotics Lab ETH Zürich Switzerland Integrated Systems Laboratory ETH Zürich Switzerland Department of Infectious Diseases Bern University Hospital University of Berm Switzerland Klinik für Urologie Unispital Zurich Switzerland Responsive Biomedical Systems Laboratory ETH Zurich Switzerland
Urine output is a vital parameter to gauge kidney health. Current monitoring methods include manually written records, invasive urinary catheterization or ultrasound measurements performed by highly skilled personnel.... 详细信息
来源: 评论
On the programming effort required to generate Behavior Trees and Finite State Machines for robotic applications
arXiv
收藏 引用
arXiv 2022年
作者: Iovino, Matteo Förster, Julian Falco, Pietro Chung, Jen Jen Siegwart, Roland Smith, Christian ABB Corporate Research Västerås Sweden Division of Robotics Perception and Learning KTH - Royal Institute of Technology Stockholm Sweden Autonomous Systems Lab ETH Zürich Zürich Switzerland School of ITEE The University of Queensland Australia
In this paper we provide a practical demonstration of how the modularity in a Behavior Tree (BT) decreases the effort in programming a robot task when compared to a Finite State Machine (FSM). In recent years the way ... 详细信息
来源: 评论
Hierarchical human-motion prediction and logic-geometric programming for minimal interference human-robot tasks
arXiv
收藏 引用
arXiv 2021年
作者: Le, An T. Kratzer, Philipp Hagenmayer, Simon Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab University of Stuttgart Germany Max Planck Institute for Intelligent Systems IS-MPI Tübingen/Stuttgart Germany Technische Universität Berlin TUB Germany
In this paper, we tackle the problem of human-robot coordination in sequences of manipulation tasks. Our approach integrates hierarchical human motion prediction with Task and Motion Planning (TAMP). We first devise a... 详细信息
来源: 评论
GraspME - Grasp manifold estimator
arXiv
收藏 引用
arXiv 2021年
作者: Hager, Janik Bauer, Ruben Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab IPVS University of Stuttgart Germany Max Planck Institute for Intelligent Systems IS-MPI Tübingen/Stuttgart Germany Technische Universität Berlin TUB Germany
In this paper, we introduce a Grasp Manifold Estimator (GraspME) to detect grasp affordances for objects directly in 2D camera images. To perform manipulation tasks autonomously it is crucial for robots to have such g... 详细信息
来源: 评论
A system for traded control teleoperation of manipulation tasks using intent prediction from hand gestures
arXiv
收藏 引用
arXiv 2021年
作者: Oh, Yoojin Schäfer, Tim Rüther, Benedikt Toussaint, Marc Mainprice, Jim Machine Learning and Robotics Lab IPVS University of Stuttgart Germany Max Planck Institute for Intelligent Systems MPI-IS Tübingen/Stuttgart Germany Technische Universität Berlin TUB Germany
This paper presents a teleoperation system that includes robot perception and intent prediction from hand gestures. The perception module identifies the objects present in the robot workspace and the intent prediction... 详细信息
来源: 评论
learning efficient constraint graph sampling for robotic sequential manipulation
arXiv
收藏 引用
arXiv 2020年
作者: Ortiz-Haro, Joaquim Hartmann, Valentin N. Oguz, Ozgur S. Toussaint, Marc Machine Learning & Robotics Lab. University of Stuttgart Germany Max Planck Institute for Intelligent Systems Germany Learning and Intelligent Systems Lab. TU Berlin Germany
Efficient sampling from constraint manifolds, and thereby generating a diverse set of solutions for feasibility problems, is a fundamental challenge. We consider the case where a problem is factored, that is, the unde... 详细信息
来源: 评论
Probabilistic Framework for Constrained Manipulations and Task and Motion Planning under Uncertainty
arXiv
收藏 引用
arXiv 2020年
作者: Ha, Jung-Su Driess, Danny Toussaint, Marc Machine Learning & Robotics Lab University Stuttgart Max Planck Institute for Intelligent Systems Stuttgart Germany
— Logic-Geometric Programming (LGP) is a powerful motion and manipulation planning framework, which represents hierarchical structure using logic rules that describe discrete aspects of problems, e.g., touch, grasp, ... 详细信息
来源: 评论
A Probabilistic Framework for Constrained Manipulations and Task and Motion Planning under Uncertainty
A Probabilistic Framework for Constrained Manipulations and ...
收藏 引用
IEEE International Conference on robotics and Automation (ICRA)
作者: Jung-Su Ha Danny Driess Marc Toussaint Machine Learning & Robotics Lab University Stuttgart and with the Max Planck Institute for Intelligent Systems Stuttgart Germany
Logic-Geometric Programming (LGP) is a powerful motion and manipulation planning framework, which represents hierarchical structure using logic rules that describe discrete aspects of problems, e.g., touch, grasp, hit... 详细信息
来源: 评论
Deep visual reasoning: learning to predict action sequences for task and motion planning from an initial scene image
arXiv
收藏 引用
arXiv 2020年
作者: Driess, Danny Ha, Jung-Su Toussaint, Marc Machine Learning and Robotics Lab University of Stuttgart Germany Max-Planck Institute for Intelligent Systems Stuttgart Germany Learning and Intelligent Systems Group TU Berlin Germany
In this paper, we propose a deep convolutional recurrent neural network that predicts action sequences for task and motion planning (TAMP) from an initial scene image. Typical TAMP problems are formalized by combining... 详细信息
来源: 评论
Natural Gradient Shared Control
Natural Gradient Shared Control
收藏 引用
IEEE International Workshop on Robot and Human Communication (ROMAN)
作者: Yoojin Oh Shao-Wen Wu Marc Toussaint Jim Mainprice Machine Learning and Robotics Lab University of Stuttgart Germany Learning and Intelligent Systems Lab TU Berlin Berlin Germany Max Planck Institute for Intelligent Systems IS-MPI Tübingen Germany
We propose a formalism for shared control, which is the problem of defining a policy that blends user control and autonomous control. The challenge posed by the shared autonomy system is to maintain user control autho...
来源: 评论