Transformers have demonstrated remarkable success in natural language
pr...
At smaller airports without an instrument approach or advanced equipment...
Bilinear dynamical systems are ubiquitous in many different domains and ...
Backward reachability analysis is essential to synthesizing controllers ...
We present a motion planning algorithm for a class of uncertain
control-...
Switched systems are capable of modeling processes with underlying dynam...
Learning how to effectively control unknown dynamical systems is crucial...
Real-world control applications often involve complex dynamics subject t...
We present a method for contraction-based feedback motion planning of lo...
Two established approaches to engineer adaptive systems are
architecture...
We present a method for learning to satisfy uncertain constraints from
d...
We present an approach for feedback motion planning of systems with unkn...
We present a method for learning multi-stage tasks from demonstrations b...
Many methods in learning from demonstration assume that the demonstrator...
We present an algorithm for learning parametric constraints from
locally...
In this paper, we consider the multi-robot path execution problem where ...
We present a scalable algorithm for learning parametric constraints in h...
We extend the learning from demonstration paradigm by providing a method...
In many multirobot applications, planning trajectories in a way to guara...
This paper employs correct-by-construction control synthesis, in particu...
We consider the problem of learning a realization for a linear time-inva...
In this paper, we consider adaptive decision-making problems for stochas...