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Found 7 papers, 5 papers with code
Enhanced Importance Sampling through Latent Space Exploration in Normalizing Flows
The latent space can be more easily explored during the search for a proposal distribution, and samples from the proposal distribution are recovered in the space of the target distribution via the invertible mapping.
Failure Probability Estimation for Black-Box Autonomous Systems using State-Dependent Importance Sampling Proposals
Estimating the probability of failure is a critical step in developing safety-critical autonomous systems.
Diffusion-Based Failure Sampling for Cyber-Physical Systems
Validating safety-critical autonomous systems in high-dimensional domains such as robotics presents a significant challenge.
Entropy-regularized Point-based Value Iteration
However, model-based planners may be brittle under these types of uncertainty because they rely on an exact model and tend to commit to a single optimal behavior.
Model-based Validation as Probabilistic Inference
Estimating the distribution over failures is a key step in validating autonomous systems.
Risk-aware Meta-level Decision Making for Exploration Under Uncertainty
Robotic exploration of unknown environments is fundamentally a problem of decision making under uncertainty where the robot must account for uncertainty in sensor measurements, localization, action execution, as well as many other factors.
How Do We Fail? Stress Testing Perception in Autonomous Vehicles
This paper presents a method for characterizing failures of LiDAR-based perception systems for AVs in adverse weather conditions.
