2 citations · 2 across the 7 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…
Task-specific Subnetwork Discovery in Reinforcement Learning for Autonomous Underwater Navigation
Yi-Ling Liu, Melvin Laux, Mariela De Lucas Alvarez +2
Autonomous underwater vehicles are required to perform multiple tasks adaptively and in an explainable manner under dynamic, uncertain conditions and limited sensing, challenges th…
DINO-Explorer: Active Underwater Discovery via Ego-Motion Compensated Semantic Predictive Coding
Yuhan Jin, Nayari Marie Lessa, Mariela De Lucas Alvarez +4
Marine ecosystem degradation necessitates continuous, scientifically selective underwater monitoring. However, most autonomous underwater vehicles (AUVs) operate as passive data lo…
Contextual Multi-Task Reinforcement Learning for Autonomous Reef Monitoring
Melvin Laux, Yi-Ling Liu, Rina Alo +4
Although autonomous underwater vehicles promise the capability of marine ecosystem monitoring, their deployment is fundamentally limited by the difficulty of controlling vehicles u…
Bayesian Inverse Physics for Neuro-Symbolic Robot Learning
Octavio Arriaga, Rebecca Adam, Melvin Laux +4
Real-world robotic applications, from autonomous exploration to assistive technologies, require adaptive, interpretable, and data-efficient learning paradigms. While deep learning…
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…
Deep Adversarial Reinforcement Learning for Object Disentangling
Melvin Laux, Oleg Arenz, Jan Peters +1
Deep learning in combination with improved training techniques and high computational power has led to recent advances in the field of reinforcement learning (RL) and to successful…