7 citations · 7 across the 4 of their papers we have counts for
5 papers
Improving Contrastive Learning on Visually Homogeneous Mars Rover Images
Isaac Ronald Ward, Charles Moore, Kai Pak +2
Contrastive learning has recently demonstrated superior performance to supervised learning, despite requiring no training labels. We explore how contrastive learning can be applied…
Mixed-domain Training Improves Multi-Mission Terrain Segmentation
Grace Vincent, Alice Yepremyan, Jingdao Chen +1
Planetary rover missions must utilize machine learning-based perception to continue extra-terrestrial exploration with little to no human presence. Martian terrain segmentation has…
Mars Terrain Segmentation with Less Labels
Edwin Goh, Jingdao Chen, Brian Wilson
Planetary rover systems need to perform terrain segmentation to identify drivable areas as well as identify specific types of soil for sample collection. The latest Martian terrain…
-MILP: Deep Space Network Scheduling via Mixed-Integer Linear Programming
Thomas Claudet, Ryan Alimo, Edwin Goh +3
This paper introduces -MILP, a powerful variant of the mixed-integer linear programming (MILP) optimization framework to solve NASA's Deep Space Network (DSN) scheduling problem…
Scheduling the NASA Deep Space Network with Deep Reinforcement Learning
Edwin Goh, Hamsa Shwetha Venkataram, Mark Hoffmann +2
With three complexes spread evenly across the Earth, NASA's Deep Space Network (DSN) is the primary means of communications as well as a significant scientific instrument for dozen…