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Found 16 papers, 6 papers with code
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.
Leveraging Compositional Methods for Modeling and Verification of an Autonomous Taxi System
We apply a compositional formal modeling and verification method to an autonomous aircraft taxi system.
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.
Online Distribution Shift Detection via Recency Prediction
When deploying modern machine learning-enabled robotic systems in high-stakes applications, detecting distribution shift is critical.
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.
Second-Order Sensitivity Analysis for Bilevel Optimization
Many existing approaches to bilevel optimization employ first-order sensitivity analysis, based on the implicit function theorem (IFT), for the lower problem to derive a gradient of the lower problem solution with respect to its parameters; this IFT gradient is then used in a first-order optimization method for the upper problem.
A Unified View of SDP-based Neural Network Verification through Completely Positive Programming
Verifying that input-output relationships of a neural network conform to prescribed operational specifications is a key enabler towards deploying these networks in safety-critical applications.
On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation
We identify an issue in recent approaches to learning-based control that reformulate systems with uncertain dynamics using a stochastic differential equation.
Sample-Efficient Safety Assurances using Conformal Prediction
When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial.
Towards Data-Driven Synthesis of Autonomous Vehicle Safety Concepts
As safety-critical autonomous vehicles (AVs) will soon become pervasive in our society, a number of safety concepts for trusted AV deployment have recently been proposed throughout industry and academia.
Local Calibration: Metrics and Recalibration
In this work, we propose the local calibration error (LCE) to span the gap between average and individual reliability.
Multimodal Deep Generative Models for Trajectory Prediction: A Conditional Variational Autoencoder Approach
Human behavior prediction models enable robots to anticipate how humans may react to their actions, and hence are instrumental to devising safe and proactive robot planning algorithms.
Learned Critical Probabilistic Roadmaps for Robotic Motion Planning
Critical PRMs are demonstrated to achieve up to three orders of magnitude improvement over uniform sampling, while preserving the guarantees and complexity of sampling-based motion planning.
Generative Modeling of Multimodal Multi-Human Behavior
This work presents a methodology for modeling and predicting human behavior in settings with N humans interacting in highly multimodal scenarios (i. e. where there are many possible highly-distinct futures).
Robotics Human-Computer Interaction
Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction
This paper presents a method for constructing human-robot interaction policies in settings where multimodality, i. e., the possibility of multiple highly distinct futures, plays a critical role in decision making.
Monte Carlo Motion Planning for Robot Trajectory Optimization Under Uncertainty
MCMP applies this CP estimation procedure to motion planning by iteratively (i) computing an (approximately) optimal path for the deterministic version of the problem (here, using the FMT* algorithm), (ii) computing the CP of this path, and (iii) inflating or deflating the obstacles by a common factor depending on whether the CP is higher or lower than a target value.
Robotics
