10 citations · 20 across the 8 of their papers we have counts for
8 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…
A Modular Approach to the Embodiment of Hand Motions from Human Demonstrations
Alexander Fabisch, Manuela Uliano, Dennis Marschner +3
Manipulating objects with robotic hands is a complicated task. Not only the fingers of the hand, but also the pose of the robot's end effector need to be coordinated. Using human d…
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 Survey of Behavior Learning Applications in Robotics -- State of the Art and Perspectives
Alexander Fabisch, Christoph Petzoldt, Marc Otto +1
Recent success of machine learning in many domains has been overwhelming, which often leads to false expectations regarding the capabilities of behavior learning in robotics. In th…
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…