Search Results - "Issac, Jan"

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  1. 1

    RMPflow: A Geometric Framework for Generation of Multitask Motion Policies by Cheng, Ching-An, Mukadam, Mustafa, Issac, Jan, Birchfield, Stan, Fox, Dieter, Boots, Byron, Ratliff, Nathan

    “…Generating robot motion for multiple tasks in dynamic environments is challenging, requiring an algorithm to respond reactively while accounting for complex…”
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    Journal Article
  2. 2

    Depth-based object tracking using a Robust Gaussian Filter by Issac, Jan, Wuthrich, Manuel, Cifuentes, Cristina Garcia, Bohg, Jeannette, Trimpe, Sebastian, Schaal, Stefan

    “…We consider the problem of model-based 3D-tracking of objects given dense depth images as input. Two difficulties preclude the application of a standard…”
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    Conference Proceeding
  3. 3

    Real-Time Perception Meets Reactive Motion Generation by Kappler, Daniel, Meier, Franziska, Issac, Jan, Mainprice, Jim, Cifuentes, Cristina Garcia, Wuthrich, Manuel, Berenz, Vincent, Schaal, Stefan, Ratliff, Nathan, Bohg, Jeannette

    Published in IEEE robotics and automation letters (01-07-2018)
    “…We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate…”
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    Journal Article
  4. 4

    Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience by Chebotar, Yevgen, Handa, Ankur, Makoviychuk, Viktor, Macklin, Miles, Issac, Jan, Ratliff, Nathan, Fox, Dieter

    “…We consider the problem of transferring policies to the real world by training on a distribution of simulated scenarios. Rather than manually tuning the…”
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    Conference Proceeding
  5. 5

    Probabilistic Articulated Real-Time Tracking for Robot Manipulation by Garcia Cifuentes, Cristina, Issac, Jan, Wuthrich, Manuel, Schaal, Stefan, Bohg, Jeannette

    Published in IEEE robotics and automation letters (01-04-2017)
    “…We propose a probabilistic filtering method which fuses joint measurements with depth images to yield a precise, real-time estimate of the end-effector pose in…”
    Get full text
    Journal Article
  6. 6

    RMP flow : A Geometric Framework for Generation of Multitask Motion Policies by Cheng, Ching-An, Mukadam, Mustafa, Issac, Jan, Birchfield, Stan, Fox, Dieter, Boots, Byron, Ratliff, Nathan

    “…Generating robot motion for multiple tasks in dynamic environments is challenging, requiring an algorithm to respond reactively while accounting for complex…”
    Get full text
    Journal Article
  7. 7

    RMPflow: A Geometric Framework for Generation of Multi-Task Motion Policies by Cheng, Ching-An, Mukadam, Mustafa, Issac, Jan, Birchfield, Stan, Fox, Dieter, Boots, Byron, Ratliff, Nathan

    Published 24-07-2020
    “…Generating robot motion for multiple tasks in dynamic environments is challenging, requiring an algorithm to respond reactively while accounting for complex…”
    Get full text
    Journal Article
  8. 8

    RMPflow: A Computational Graph for Automatic Motion Policy Generation by Cheng, Ching-An, Mukadam, Mustafa, Issac, Jan, Birchfield, Stan, Fox, Dieter, Boots, Byron, Ratliff, Nathan

    Published 16-11-2018
    “…We develop a novel policy synthesis algorithm, RMPflow, based on geometrically consistent transformations of Riemannian Motion Policies (RMPs). RMPs are a…”
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    Journal Article
  9. 9

    Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience by Chebotar, Yevgen, Handa, Ankur, Makoviychuk, Viktor, Macklin, Miles, Issac, Jan, Ratliff, Nathan, Fox, Dieter

    Published 12-10-2018
    “…We consider the problem of transferring policies to the real world by training on a distribution of simulated scenarios. Rather than manually tuning the…”
    Get full text
    Journal Article
  10. 10

    Riemannian Motion Policies by Ratliff, Nathan D, Issac, Jan, Kappler, Daniel, Birchfield, Stan, Fox, Dieter

    Published 09-01-2018
    “…We introduce the Riemannian Motion Policy (RMP), a new mathematical object for modular motion generation. An RMP is a second-order dynamical system…”
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    Journal Article
  11. 11

    Robust Gaussian filtering using a pseudo measurement by Wuthrich, Manuel, Cifuentes, Cristina Garcia, Trimpe, Sebastian, Meier, Franziska, Bohg, Jeannette, Issac, Jan, Schaal, Stefan

    Published in 2016 American Control Conference (ACC) (01-07-2016)
    “…Many sensors, such as range, sonar, radar, GPS and visual devices, produce measurements which are contaminated by outliers. This problem can be addressed by…”
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    Conference Proceeding Journal Article
  12. 12

    Probabilistic Articulated Real-Time Tracking for Robot Manipulation by Cifuentes, Cristina Garcia, Issac, Jan, Wüthrich, Manuel, Schaal, Stefan, Bohg, Jeannette

    Published 16-10-2016
    “…We propose a probabilistic filtering method which fuses joint measurements with depth images to yield a precise, real-time estimate of the end-effector pose in…”
    Get full text
    Journal Article
  13. 13

    Depth-Based Object Tracking Using a Robust Gaussian Filter by Issac, Jan, Wüthrich, Manuel, Cifuentes, Cristina Garcia, Bohg, Jeannette, Trimpe, Sebastian, Schaal, Stefan

    Published 19-02-2016
    “…We consider the problem of model-based 3D-tracking of objects given dense depth images as input. Two difficulties preclude the application of a standard…”
    Get full text
    Journal Article
  14. 14

    Autonomous acquisition of visual multi-view object representations for object recognition on a humanoid robot by Welke, Kai, Issac, Jan, Schiebener, David, Asfour, Tamim, Dillmann, Rüdiger

    “…The autonomous acquisition of object representations which allow recognition, localization and grasping of objects in the environment is a challenging task,…”
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    Conference Proceeding
  15. 15

    Robust Gaussian Filtering using a Pseudo Measurement by Wüthrich, Manuel, Cifuentes, Cristina Garcia, Trimpe, Sebastian, Meier, Franziska, Bohg, Jeannette, Issac, Jan, Schaal, Stefan

    Published 14-09-2015
    “…Many sensors, such as range, sonar, radar, GPS and visual devices, produce measurements which are contaminated by outliers. This problem can be addressed by…”
    Get full text
    Journal Article
  16. 16

    Real-time Perception meets Reactive Motion Generation by Kappler, Daniel, Meier, Franziska, Issac, Jan, Mainprice, Jim, Cifuentes, Cristina Garcia, Wüthrich, Manuel, Berenz, Vincent, Schaal, Stefan, Ratliff, Nathan, Bohg, Jeannette

    Published 09-03-2017
    “…We address the challenging problem of robotic grasping and manipulation in the presence of uncertainty. This uncertainty is due to noisy sensing, inaccurate…”
    Get full text
    Journal Article