Temporal logics (TLs) have been widely used to formalize interpretable t...
Control Barrier Functions (CBF) are a powerful tool for designing
safety...
Micron-scale robots (ubots) have recently shown great promise for emergi...
Sampling-based algorithms, such as Rapidly Exploring Random Trees (RRT) ...
We present a computational framework for synthesis of distributed contro...
This paper studies the problem of utilizing data-driven adaptive control...
We present a Deep Reinforcement Learning (DRL) algorithm for a task-guid...
Real-time and human-interpretable decision-making in cyber-physical syst...
Learning dynamical systems properties from data provides important insig...
Time-series data classification is central to the analysis and control o...
Autonomous vehicles must balance a complex set of objectives. There is n...
We develop optimal control strategies for autonomous vehicles (AVs) that...
Many autonomous systems, such as robots and self-driving cars, involve
r...
This paper presents a novel two-level control architecture for a fully
a...
This paper studies the problem of developing an approximate dynamic
prog...
We propose a framework for solving control synthesis problems for multi-...
We propose a policy search approach to learn controllers from specificat...
This paper addresses the problem of safety-critical control for systems ...
This paper focuses on the trajectory tracking control problem for an
art...
We develop optimal control strategies for Autonomous Vehicles (AVs) that...
It has been shown that satisfying state and control constraints while
op...
We propose a framework based on Recurrent Neural Networks (RNNs) to dete...
Recent work showed that stabilizing affine control systems to desired (s...
In this work we consider the multi-image object matching problem, extend...
We propose a new robustness score for continuous-time Signal Temporal Lo...
Robot motion planning is central to real-world autonomous applications, ...
Using reinforcement learning to learn control policies is a challenge wh...
We present a new average-based robustness score for Signal Temporal Logi...
This paper extends control barrier functions (CBFs) to high order contro...
This paper presents an application of specification based runtime
verifi...
Tasks with complex temporal structures and long horizons pose a challeng...
Signal Temporal Logic (STL) is a formal language for describing a broad ...
An obstacle that prevents the wide adoption of (deep) reinforcement lear...
Reward engineering is an important aspect of reinforcement learning. Whe...
Reinforcement learning (RL) depends critically on the choice of reward
f...
Reinforcement learning has been applied to many interesting problems suc...
We propose a technique to detect and generate patterns in a network of
l...
We present a new temporal logic called Distribution Temporal Logic (DTL)...