109 citations · 139 across the 4 of their papers we have counts for
5 papers
Adversarial Reinforcement Learning for Robust Planetary Landing
Miguel Leiva-Vélez, Nicolas Gaston-Rozado, Andrea Scorsoglio +3
Deep reinforcement learning (DRL) policies for planetary landing and other aerospace guidance, navigation, and control tasks are commonly trained under randomized disturbances to i…
Action Chunking with Transformers for Image-Based Spacecraft Guidance and Control
Alejandro Posadas-Nava, Andrea Scorsoglio, Luca Ghilardi +2
We present an imitation learning approach for spacecraft guidance, navigation, and control(GNC) that achieves high performance from limited data. Using only 100 expert demonstratio…
Breaking the Barrier for Minimum -cut on Simple Graphs
Zhiyang He, Jason Li
In the minimum -cut problem, we want to find the minimum number of edges whose deletion breaks the input graph into at least connected components. The classic algorithm of K…
Reinforcement Meta-Learning for Interception of Maneuvering Exoatmospheric Targets with Parasitic Attitude Loop
Brian Gaudet, Roberto Furfaro, Richard Linares +1
We use Reinforcement Meta-Learning to optimize an adaptive integrated guidance, navigation, and control system suitable for exoatmospheric interception of a maneuvering target. The…
Adaptive Generalized ZEM-ZEV Feedback Guidance for Planetary Landing via a Deep Reinforcement Learning Approach
Roberto Furfaro, Andrea Scorsoglio, Richard Linares +1
Precision landing on large and small planetary bodies is a technology of utmost importance for future human and robotic exploration of the solar system. In this context, the Zero-E…