Search Results for author:
Found 11 papers, 3 papers with code
Globally Convergent Policy Search over Dynamic Filters for Output Estimation
In this paper, we provide a new perspective on this challenging problem based on the notion of $\textit{informativity}$, which intuitively requires that all components of a filter's internal state are representative of the true state of the underlying dynamical system.
Stabilizing Dynamical Systems via Policy Gradient Methods
Stabilizing an unknown control system is one of the most fundamental problems in control systems engineering.
Distributed Identification of Contracting and/or Monotone Network Dynamics
This paper proposes methods for identification of large-scale networked systems with guarantees that the resulting model will be contracting -- a strong form of nonlinear stability -- and/or monotone, i. e. order relations between states are preserved.
Shortest Paths in Graphs of Convex Sets
Given a graph, the shortest-path problem requires finding a sequence of edges with minimum cumulative length that connects a source vertex to a target vertex.
Robot Navigation
Discrete Mathematics
Optimization and Control
Optimistic robust linear quadratic dual control
Recent work by Mania et al. has proved that certainty equivalent control achieves nearly optimal regret for linear systems with quadratic costs.
Robust exploration in linear quadratic reinforcement learning
This paper concerns the problem of learning control policies for an unknown linear dynamical system to minimize a quadratic cost function.
On the smoothness of nonlinear system identification
We shed new light on the \textit{smoothness} of optimization problems arising in prediction error parameter estimation of linear and nonlinear systems.
Nonlinear input design as optimal control of a Hamiltonian system
We propose an input design method for a general class of parametric probabilistic models, including nonlinear dynamical systems with process noise.
Learning convex bounds for linear quadratic control policy synthesis
Learning to make decisions from observed data in dynamic environments remains a problem of fundamental importance in a number of fields, from artificial intelligence and robotics, to medicine and finance.
Specialized Interior Point Algorithm for Stable Nonlinear System Identification
Estimation of nonlinear dynamic models from data poses many challenges, including model instability and non-convexity of long-term simulation fidelity.
