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Found 18 papers, 2 papers with code
Fault Tolerant Neural Control Barrier Functions for Robotic Systems under Sensor Faults and Attacks
Control barrier function (CBF)-based approaches have been proposed to guarantee the safety of robotic systems.
Neural Lyapunov Control for Discrete-Time Systems
However, finding Lyapunov functions for general nonlinear systems is a challenging task.
Pre-processing training data improves accuracy and generalisability of convolutional neural network based landscape semantic segmentation
Our results showed: a stratified random sampling approach for producing training patches improved the accuracy of classes with a smaller area while having minimal effect on larger classes; a smaller number of larger patches compared to a larger number of smaller patches improves model accuracy; applying data augmentations and scaling are imperative in creating a generalised model able to accurately classify LULC features in imagery from a different date and sensor; and producing the output classification by averaging multiple grids of patches and three rotated versions of each patch produced and more accurate and aesthetic result.
Risk-Aware Distributed Multi-Agent Reinforcement Learning
We then propose a distributed MARL algorithm called the CVaR QD-Learning algorithm, and establish that value functions of individual agents reaches consensus.
Cooperative Perception for Safe Control of Autonomous Vehicles under LiDAR Spoofing Attacks
In this paper, we propose an approach to detect and mitigate LiDAR spoofing attacks by leveraging LiDAR scan data from other neighboring vehicles.
A Semi-Algebraic Framework for Verification and Synthesis of Control Barrier Functions
Based on these conditions, we propose a framework for verifying safety of CBF-based control including single CBFs, high-order CBFs, multi-CBFs, and systems with trigonometric dynamics and actuation constraints.
Abstraction-Free Control Synthesis to Satisfy Temporal Logic Constraints under Sensor Faults and Attacks
We develop a class of fault-tolerant finite time convergence control barrier functions (CBFs) to guarantee that a dynamical system reaches a set within finite time almost surely in the presence of malicious attacks.
Barrier Certificate based Safe Control for LiDAR-based Systems under Sensor Faults and Attacks
We prove that the synthesized control input guarantees system safety using control barrier certificates.
Safe Control for Nonlinear Systems under Faults and Attacks via Control Barrier Functions
Safety is one of the most important properties of control systems.
Safety-Critical Control Synthesis for Unknown Sampled-Data Systems via Control Barrier Functions
By satisfying the constructed CBF constraint at each sampling time, we guarantee the unknown sampled-data system is safe for all time.
A Game-Theoretic Framework for Controlled Islanding in the Presence of Adversaries
In this paper, we study the controlled islanding problem of a power system under disturbances introduced by a malicious adversary.
Verification and Synthesis of Control Barrier Functions
Our approach is to show that safety of CBFs is equivalent to the non-existence of solutions to a family of polynomial equations, and then prove that this nonexistence is equivalent to a pair of sum-of-squares constraints via the Positivstellensatz of algebraic geometry.
Reinforcement Learning Beyond Expectation
In this paper, we consider a setting where an autonomous agent has to learn behaviors in an unknown environment.
LQG Reference Tracking with Safety and Reachability Guarantees under Unknown False Data Injection Attacks
For each possible set of compromised sensors, we maintain a state estimator disregarding the sensors in that set, and calculate the optimal LQG control input at each time based on this estimate.
FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback
The output of the feedback neural network is converted to a shaping reward that is augmented to the reward provided by the environment.
Potential-Based Advice for Stochastic Policy Learning
This paper augments the reward received by a reinforcement learning agent with potential functions in order to help the agent learn (possibly stochastic) optimal policies.
Attacking Automatic Video Analysis Algorithms: A Case Study of Google Cloud Video Intelligence API
At the end, we propose introducing randomness to video analysis algorithms as a countermeasure to our attacks.
Group theory, group actions, evolutionary algorithms, and global optimization
In this paper we use group, action and orbit to understand how evolutionary solve nonconvex optimization problems.
