Zobrazeno 1 - 10
of 355
pro vyhledávání: '"Beck, Florian"'
Combining a vision module inside a closed-loop control system for a \emph{seamless movement} of a robot in a manipulation task is challenging due to the inconsistent update rates between utilized modules. This task is even more difficult in a dynamic
Externí odkaz:
http://arxiv.org/abs/2406.09039
Model Predictive Trajectory Optimization With Dynamically Changing Waypoints for Serial Manipulators
Systematically including dynamically changing waypoints as desired discrete actions, for instance, resulting from superordinate task planning, has been challenging for online model predictive trajectory optimization with short planning horizons. This
Externí odkaz:
http://arxiv.org/abs/2402.04730
This work presents a novel online model-predictive trajectory planner for robotic manipulators called BoundMPC. This planner allows the collision-free following of Cartesian reference paths in the end-effector's position and orientation, including vi
Externí odkaz:
http://arxiv.org/abs/2401.05057
We study mathematical structures on the moduli spaces of BPS structures of $\mathcal{N}=2$ theories. Guided by the realization of BPS structures within type IIB string theory on non-compact Calabi-Yau threefolds, we develop a notion of BPS variation
Externí odkaz:
http://arxiv.org/abs/2308.16854
Publikováno v:
Mach Learn (2023)
Conventional rule learning algorithms aim at finding a set of simple rules, where each rule covers as many examples as possible. In this paper, we argue that the rules found in this way may not be the optimal explanations for each of the examples the
Externí odkaz:
http://arxiv.org/abs/2301.09936
This paper presents a two-step algorithm for online trajectory planning in indoor environments with unknown obstacles. In the first step, sampling-based path planning techniques such as the optimal Rapidly exploring Random Tree (RRT*) algorithm and t
Externí odkaz:
http://arxiv.org/abs/2211.06377
Autor:
Vu, Minh Nhat, Beck, Florian, Schwegel, Michael, Hartl-Nesic, Christian, Nguyen, Anh, Kugi, Andreas
Publikováno v:
Mechatronics Volume 91, May 2023, 102970
Solving the analytical inverse kinematics (IK) of redundant manipulators in real time is a difficult problem in robotics since its solution for a given target pose is not unique. Moreover, choosing the optimal IK solution with respect to application-
Externí odkaz:
http://arxiv.org/abs/2211.04275
This work proposes a novel singularity avoidance approach for real-time trajectory optimization based on known singular configurations. The focus of this work lies on analyzing kinematically singular configurations for three robots with different kin
Externí odkaz:
http://arxiv.org/abs/2211.02516
Autor:
Beck, Florian, Fürnkranz, Johannes
Inductive rule learning is arguably among the most traditional paradigms in machine learning. Although we have seen considerable progress over the years in learning rule-based theories, all state-of-the-art learners still learn descriptions that dire
Externí odkaz:
http://arxiv.org/abs/2106.10254
Autor:
Beck, Florian, Fürnkranz, Johannes
Publikováno v:
2nd Workshop on Deep Continuous-Discrete Machine Learning (DeCoDeML), ECML-PKDD 2020, Ghent, Belgium
We investigate whether it is possible to learn rule sets efficiently in a network structure with a single hidden layer using iterative refinements over mini-batches of examples. A first rudimentary version shows an acceptable performance on all but o
Externí odkaz:
http://arxiv.org/abs/2106.10202