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Found 8 papers, 0 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.
Real-Time Anomaly Detection and Reactive Planning with Large Language Models
Foundation models, e. g., large language models (LLMs), trained on internet-scale data possess zero-shot generalization capabilities that make them a promising technology towards detecting and mitigating out-of-distribution failure modes of robotic systems.
Learning Temporal Logic Predicates from Data with Statistical Guarantees
We present a novel method to learn temporal logic predicates from data with finite-sample correctness guarantees.
Closing the Loop on Runtime Monitors with Fallback-Safe MPC
When we rely on deep-learned models for robotic perception, we must recognize that these models may behave unreliably on inputs dissimilar from the training data, compromising the closed-loop system's safety.
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.
Adaptive Robust Model Predictive Control via Uncertainty Cancellation
We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component.
Online Distribution Shift Detection via Recency Prediction
When deploying modern machine learning-enabled robotic systems in high-stakes applications, detecting distribution shift is critical.
Adaptive Robust Model Predictive Control with Matched and Unmatched Uncertainty
We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component.
