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cs.RO2026

Hardware-Accelerated Instance Segmentation for Resource-Constrained Space Robotics with Criticality Analysis

Siddhant Shete, Hilmi Dogu Kücüker, Udo Frese +1

Autonomous lunar missions require real-time per- ception under three coupled constraints: extreme low-light conditions, limited onboard compute, and radiation-induced hardware faul…

cs.LG2026

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…

cs.LG2026

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…

cs.RO2026

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…

cs.RO2026

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…