5 citations · 8 across the 5 of their papers we have counts for
6 papers
Attribution and Uncertainty Behavior of Learned Residual Gyro Correction for Gyro-Stellar Estimation
Mariela De Lucas Álvarez, Melvin Laux, Arthur de Freitas Precht +4
This work investigates uncertainty decomposition and explainability in a deep learning-based framework for gyroscope bias correction. A 1-D Convolutional Neural Network is trained…
Deep Reinforcement Learning for Spacecraft Attitude Control During Atmospheric Re-Entry
Alexander Fabisch, Melvin Laux, Mariela De Lucas Álvarez +2
Deep reinforcement learning has the potential to solve attitude control problems more adaptively, precisely, and robustly by handling nonlinear dynamics, uncertainties, and failure…
Do Robots Really Need Anthropomorphic Hands? A Comparison of Human and Robotic Hands
Alexander Fabisch, Wadhah Zai El Amri, Chandandeep Singh +1
Human manipulation skills represent a pinnacle of voluntary motor functions, requiring the coordination of many degrees of freedom and the processing of high-dimensional sensor inp…
Comparison of Distal Teacher Learning with Numerical and Analytical Methods to Solve Inverse Kinematics for Rigid-Body Mechanisms
Tim von Oehsen, Alexander Fabisch, Shivesh Kumar +1
Several publications are concerned with learning inverse kinematics, however, their evaluation is often limited and none of the proposed methods is of practical relevance for rigid…
A Comparison of Policy Search in Joint Space and Cartesian Space for Refinement of Skills
Alexander Fabisch
Imitation learning is a way to teach robots skills that are demonstrated by humans. Transfering skills between these different kinematic structures seems to be straightforward in C…
Empirical Evaluation of Contextual Policy Search with a Comparison-based Surrogate Model and Active Covariance Matrix Adaptation
Alexander Fabisch
Contextual policy search (CPS) is a class of multi-task reinforcement learning algorithms that is particularly useful for robotic applications. A recent state-of-the-art method is…