6 papers
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…
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…
Differentiable Inverse Graphics for Zero-shot Scene Reconstruction and Robot Grasping
Octavio Arriaga, Proneet Sharma, Jichen Guo +3
Operating effectively in novel real-world environments requires robotic systems to estimate and interact with previously unseen objects. Current state-of-the-art models address thi…
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…
Bayesian Inverse Graphics for Few-Shot Concept Learning
Octavio Arriaga, Jichen Guo, Rebecca Adam +2
Humans excel at building generalizations of new concepts from just one single example. Contrary to this, current computer vision models typically require large amount of training s…