We consider data-driven reachability analysis of discrete-time stochasti...
Many modern autonomous systems, particularly multi-agent systems, are
ti...
The interest in using reinforcement learning (RL) controllers in
safety-...
Conformal prediction is a statistical tool for producing prediction regi...
We consider perception-based control using state estimates that are obta...
Predicting the motion of dynamic agents is a critical task for guarantee...
This paper proposes an algorithm for motion planning among dynamic agent...
We are interested in predicting failures of cyber-physical systems durin...
We propose a framework for planning in unknown dynamic environments with...
Many future technologies rely on neural networks, but verifying the
corr...
The wide availability of data coupled with the computational advances in...
This paper addresses learning safe control laws from expert demonstratio...
The deployment of autonomous systems in uncertain and dynamic environmen...
Motion planning is a fundamental problem and focuses on finding control
...
We present a framework to interpret signal temporal logic (STL) formulas...
Motivated by the recent interest in cyber-physical and autonomous roboti...
The need for robust control laws is especially important in safety-criti...
Motivated by the lack of systematic tools to obtain safe control laws fo...
We propose a hybrid feedback control law that guarantees both safety and...
Inspired by the success of imitation and inverse reinforcement learning ...