Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Torsten Kröger"'
Publikováno v:
Intelligent Autonomous Systems 17 ISBN: 9783031222153
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ec3fa975486ace2f77573f9fdb1a4c07
https://doi.org/10.1007/978-3-031-22216-0_7
https://doi.org/10.1007/978-3-031-22216-0_7
Publikováno v:
2022 IEEE-RAS 21st International Conference on Humanoid Robots (Humanoids).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c7e782b430dd4ad28d29429d11f906ad
https://doi.org/10.1109/humanoids53995.2022.10000206
https://doi.org/10.1109/humanoids53995.2022.10000206
Publikováno v:
IEEE Robotics and Automation Letters. 6:431-438
Using a closed-form inverse kinematics solution for motion planning has many advantages compared to traditional numerical approaches, most notably much faster computation times and better suitability for real-time applications. Steady progress has be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2a59c7311fc9bd8b81d4584062a6027b
https://doi.org/10.1109/lra.2020.3045646
https://doi.org/10.1109/lra.2020.3045646
Publikováno v:
IEEE Robotics and Automation Letters. 5:4828-4835
Flexible pick-and-place is a fundamental yet challenging task within robotics, in particular due to the need of an object model for a simple target pose definition. In this work, the robot instead learns to pick-and-place objects using planar manipul
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c2b98dcb087e58f027ead9435dc58c2
https://doi.org/10.1109/lra.2020.3003865
https://doi.org/10.1109/lra.2020.3003865
Publikováno v:
University of Aberdeen-PURE
Robot learning of real-world manipulation tasks remains challenging and time consuming, even though actions are often simplified by single-step manipulation primitives. In order to compensate the removed time dependency, we additionally learn an imag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2ef5ccd58455de213d57fbdc5af6f6d6
https://doi.org/10.1109/iros51168.2021.9636623
https://doi.org/10.1109/iros51168.2021.9636623
Publikováno v:
ICRA
Robot learning is often simplified to planar manipulation due to its data consumption. Then, a common approach is to use a fully-convolutional neural network to estimate the reward of grasp primitives. In this work, we extend this approach by paramet
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a77eca4e1e3c41957a86c050bad50cd8
http://arxiv.org/abs/2103.12810
http://arxiv.org/abs/2103.12810
Publikováno v:
ICRA
During the planning phase of industrial robot workplaces, hazard analyses are required so that potential hazards for human workers can be identified and appropriate safety measures can be implemented. Existing hazard analysis methods use human reason
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f8d233a59c97b111c5b69b862940c8bc
http://arxiv.org/abs/2103.00973
http://arxiv.org/abs/2103.00973
Autor:
Torsten Kröger, Jonas C. Kiemel
Publikováno v:
ICRA
We present an approach to learn fast and dynamic robot motions without exceeding limits on the position $\theta$, velocity $\dot{\theta}$, acceleration $\ddot{\theta}$ and jerk $\dddot{\theta}$ of each robot joint. Movements are generated by mapping
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::752cca71168a773761ab257a9fbeb755
http://arxiv.org/abs/2011.00563
http://arxiv.org/abs/2011.00563
Publikováno v:
IROS
We present TrueAEdapt, a model-free method to learn online adaptations of robot trajectories based on their effects on the environment. Given sensory feedback and future waypoints of the original trajectory, a neural network is trained to predict joi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f0dc61308cfa592a837c9a773d05b164
https://doi.org/10.1109/iros45743.2020.9341001
https://doi.org/10.1109/iros45743.2020.9341001
Publikováno v:
ICRA
We present TrueRMA, a data-efficient, model-free method to learn cost-optimized robot trajectories over a wide range of starting points and endpoints. The key idea is to calculate trajectory waypoints in Cartesian space by recursively predicting orth
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9c2a3dfb265b1c91285758ebe762e4ce
http://arxiv.org/abs/2006.03497
http://arxiv.org/abs/2006.03497