Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions

Reinforcement learning (RL) exhibits impressive performance when managing complicated control tasks for robots. However, its wide application to physical robots is limited by the absence of strong safety guarantees. To overcome this challenge, this paper explores the control Lyapunov barrier function (CLBF) to analyze the safety and reachability...

Detection of False Data Injection Attacks Using the Autoencoder Approach

State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids....

Training Strategies for Autoencoder-based Detection of False Data Injection Attacks

The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to interfere the validity of estimated states. In this paper, we use an autoencoder neural network to...

Rope Caging and Grasping

We present a novel method for caging grasps in this paper by stretching ropes on the surface of a 3D object. Both topology and shape of a model to be grasped has been<br/>considered in our approach. Our algorithm can guarantee generating local minimal rings on every topological branches of a given model with the help of a Reeb graph. Cages and...