This tutorial serves as an introduction to recently developed non-asympt...
With the increase in data availability, it has been widely demonstrated ...
A powerful concept behind much of the recent progress in machine learnin...
A learning-based modular motion planning pipeline is presented that is
c...
We consider joint trajectory generation and tracking control for
under-a...
We derive upper bounds for random design linear regression with dependen...
A common pipeline in learning-based control is to iteratively estimate a...
We study representation learning for efficient imitation learning over l...
Nonlinear model predictive control (MPC) is a flexible and increasingly
...
We consider how to most efficiently leverage teleoperator time to collec...
This tutorial survey provides an overview of recent non-asymptotic advan...
We study stochastic policy gradient methods from the perspective of
cont...
We propose Taylor Series Imitation Learning (TaSIL), a simple augmentati...
The wide availability of data coupled with the computational advances in...
In this paper, we study the statistical difficulty of learning to contro...
We consider the problems of exploration and point-goal navigation in
pre...
Given a single trajectory of a dynamical system, we analyze the performa...
We introduce Variational State-Space Filters (VSSF), a new method for
un...
Motivated by bridging the simulation to reality gap in the context of
sa...
This paper addresses learning safe control laws from expert demonstratio...
When designing large-scale distributed controllers, the information-shar...
We present a framework to interpret signal temporal logic (STL) formulas...
The difficulty of optimal control problems has classically been characte...
Commonly used optimization-based control strategies such as model-predic...
The need for robust control laws is especially important in safety-criti...
We establish data-driven versions of the System Level Synthesis (SLS)
pa...
Motivated by the lack of systematic tools to obtain safe control laws fo...
Many existing tools in nonlinear control theory for establishing stabili...
Traditionally, controllers and state estimators in robotic systems are
d...
Inspired by the success of imitation and inverse reinforcement learning ...
Good prediction is necessary for autonomous robotics to make informed
de...
We propose an algorithm combining calibrated prediction and generalizati...
In this paper, we consider the task of designing a Kalman Filter (KF) fo...
In this work, we propose a robust approach to design distributed control...
Motivated by vision based control of autonomous vehicles, we consider th...
We provide a brief tutorial on the use of concentration inequalities as ...
Machine and reinforcement learning (RL) are being applied to plan and co...
This paper addresses the problem of identifying sparse linear time-invar...
We study the constrained linear quadratic regulator with unknown dynamic...
We consider adaptive control of the Linear Quadratic Regulator (LQR), wh...
As the systems we control become more complex, first-principle modeling
...
This paper addresses the optimal control problem known as the Linear
Qua...
This paper proposes a new method for rigid body pose estimation based on...