A semi-algebraic framework for verification and synthesis of control barrier functions
A Clark - IEEE Transactions on Automatic Control, 2024 - ieeexplore.ieee.org
Safety is a critical property for control systems in medicine, transportation, manufacturing,
and other applications, and can be defined as ensuring positive invariance of a predefined …
and other applications, and can be defined as ensuring positive invariance of a predefined …
Constrained environment optimization for prioritized multi-agent navigation
Traditional approaches for multi-agent navigation consider the environment as a fixed
constraint, despite the obvious influence of spatial constraints on agents' performance. Yet …
constraint, despite the obvious influence of spatial constraints on agents' performance. Yet …
Co-Optimization of Environment and Policies for Decentralized Multi-Agent Navigation
This work views the multi-agent system and its surrounding environment as a co-evolving
system, where the behavior of one affects the other. The goal is to take both agent actions …
system, where the behavior of one affects the other. The goal is to take both agent actions …
Learning to Refine Input Constrained Control Barrier Functions via Uncertainty-Aware Online Parameter Adaptation
Control Barrier Functions (CBFs) have become powerful tools for ensuring safety in
nonlinear systems. However, finding valid CBFs that guarantee persistent safety and …
nonlinear systems. However, finding valid CBFs that guarantee persistent safety and …
The Cambridge RoboMaster: An Agile Multi-Robot Research Platform
Compact robotic platforms with powerful compute and actuation capabilities are key
enablers for practical, real-world deployments of multi-agent research. This article …
enablers for practical, real-world deployments of multi-agent research. This article …
Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learning
Breaking safety constraints in control systems can lead to potential risks, resulting in
unexpected costs or catastrophic damage. Nevertheless, uncertainty is ubiquitous, even …
unexpected costs or catastrophic damage. Nevertheless, uncertainty is ubiquitous, even …
Learning-Based Control Barrier Function with Provably Safe Guarantees: Reducing Conservatism with Heading-Aware Safety Margin
J Xu, B Alrifaee - arXiv preprint arXiv:2411.08999, 2024 - arxiv.org
We propose a learning-based Control Barrier Function (CBF) to reduce conservatism in
collision avoidance of car-like robots. Traditional CBFs often use Euclidean distance …
collision avoidance of car-like robots. Traditional CBFs often use Euclidean distance …
Incremental Composition of Learned Control Barrier Functions in Unknown Environments
We consider the problem of safely exploring a static and unknown environment while
learning valid control barrier functions (CBFs) from sensor data. Existing works either …
learning valid control barrier functions (CBFs) from sensor data. Existing works either …
Safe Decentralized Multi-Agent Control using Black-Box Predictors, Conformal Decision Policies, and Control Barrier Functions
We address the challenge of safe control in decentralized multi-agent robotic settings, where
agents use uncertain black-box models to predict other agents' trajectories. We use the …
agents use uncertain black-box models to predict other agents' trajectories. We use the …
On the Trade-Off between Stability and Representational Capacity in Graph Neural Networks
Analyzing the stability of graph neural networks (GNNs) under topological perturbations is
key to understanding their transferability and the role of each architecture component …
key to understanding their transferability and the role of each architecture component …