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Found 5 papers, 2 papers with code
Adapting a Foundation Model for Space-based Tasks
Foundation models, e. g., large language models, possess attributes of intelligence which offer promise to endow a robot with the contextual understanding necessary to navigate complex, unstructured tasks in the wild.
Diagnostic Runtime Monitoring with Martingales
Machine learning systems deployed in safety-critical robotics settings must be robust to distribution shifts.
Contingency Planning Using Bi-level Markov Decision Processes for Space Missions
This work focuses on autonomous contingency planning for scientific missions by enabling rapid policy computation from any off-nominal point in the state space in the event of a delay or deviation from the nominal mission plan.
A System-Level View on Out-of-Distribution Data in Robotics
When testing conditions differ from those represented in training data, so-called out-of-distribution (OOD) inputs can mar the reliability of learned components in the modern robot autonomy stack.
Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications
Algorithms within this framework are evaluated based on (1) model performance throughout mission lifetime and (2) cumulative costs associated with labeling and model retraining.
