5 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…
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…
Safety Enhancement in Planetary Rovers: Early Detection of Tip-over Risks Using Autoencoders
Mariela De Lucas Alvarez
Autonomous robots consistently encounter unforeseen dangerous situations during exploration missions. The characteristic rimless wheels in the AsguardIV rover allow it to overcome…
Terrain Classification Enhanced with Uncertainty for Space Exploration Robots from Proprioceptive Data
Mariela De Lucas Álvarez, Jichen Guo, Raul Domínguez +1
Terrain Classification is an essential task in space exploration, where unpredictable environments are difficult to observe using only exteroceptive sensors such as vision. Impleme…